LLM Fairness Dashboard

Bank Complaint Handling Fairness Analysis

Generated: 2025-09-22T09:38:54.205746 | Total Experiments: 2,000

0.526

Zero-Shot Accuracy

0.467

N-Shot Accuracy

2,000

Sample Size

Headline Results

Persona Injection

Severity and Bias

Bias Mitigation

Ground Truth Accuracy

Executive Summary

Statistically Significant and Material (14)

Statistically Significant but Trivial (15)

Executive Summary

AI-powered analysis of fairness testing results for bank complaint handling system

Key Findings Overview

The fairness testing of our LLM-based complaint handling system has revealed significant bias patterns that require immediate attention. Notably, the severity-dependent bias in persona injection, with a stark effect size of 2.238675412029951 and 1.9944937467091666, indicates a critical need to recalibrate our LLM's response mechanisms based on complaint severity. Geographic disparities, particularly the 481.0% difference in question rates between suburban poor and urban working-class demographics, underscore a profound socioeconomic bias. Furthermore, method inconsistencies between zero-shot and n-shot learning methods, as evidenced by varying gender and ethnicity biases, highlight a complex layer of bias that complicates the LLM's decision-making process.

Financial Services Industry Implications

The identified biases pose significant regulatory and operational risks, potentially contravening the Fair Lending Act and CFPB enforcement priorities. The severity-dependent bias could lead to unequal treatment of complaints, affecting customer satisfaction and trust, and potentially resulting in regulatory scrutiny. Geographic and socioeconomic disparities raise concerns about equitable access and treatment across different demographics, risking reputational damage and legal challenges. Method inconsistencies in handling complaints could further complicate compliance efforts, making it difficult to ensure consistent and fair treatment of all customers.

Strategic Recommendations

To mitigate these risks, we recommend prioritizing the governance of high-stakes decisions, particularly where severity-dependent bias was identified, by implementing a tiered review system that escalates more severe complaints for human review. Addressing process bias in addition to outcome bias is crucial; thus, standardizing question rates across demographics by adjusting the LLM's prompting methods can help achieve more equitable treatment. Expanding bias testing beyond traditional demographics to include geographic and socioeconomic factors will ensure a more comprehensive understanding of bias within our system. Utilizing effect size filtering to prioritize bias risks will allow us to focus our efforts on the most impactful disparities. Finally, addressing method-dependent bias inconsistencies by developing a hybrid model that combines the strengths of both zero-shot and n-shot methods could offer a more balanced and fair approach to complaint handling.

10

Material Findings

19

Trivial Findings

29

Total Findings

Key Findings with Practical Importance

14 findings that are both statistically significant and practically important

These results represent real, meaningful differences that impact fairness in complaint handling.

Sort by:

#1 Severity and Bias → Tier Impact Large Effect

Persona injection bias differs between severity levels (χ² = 19.097)

Test: Tier Impact Rate: Zero-Shot

p-value: < 0.0001

Effect Size: cohens_d = 2.239

Sample: n = 10000

What this means:

There is strong evidence that bias is greater for more severe cases.

Financial Services Impact

The statistical findings indicate a significant bias in the bank's LLM complaint handling system, particularly affecting the treatment of complaints of varying severity. With a p-value far below the standard threshold of 0.05 and a large Cohen's d effect size, this bias is not only statistically significant but also practically meaningful. This suggests that the system's responses to complaints may disproportionately disadvantage or advantage certain demographic groups, especially in more severe cases. Such disparities could lead to regulatory scrutiny under the Fair Lending Act, which mandates equitable treatment across all customer segments. Additionally, the Consumer Financial Protection Bureau (CFPB) has been increasingly focused on the fairness of AI systems in financial services, making this finding a potential trigger for enforcement actions, including fines and mandates for corrective measures.

Customer Experience Implications

Although the specific demographic groups affected by this bias are not detailed in the provided findings, the presence of persona injection bias suggests that certain characteristics (e.g., age, gender, race, or income level) could influence the system's severity assessment and subsequent handling of complaints. This could lead to a perception of unfair treatment among those negatively impacted, eroding trust not only in the bank's complaint resolution process but also in its broader commitment to equitable customer service. For groups that are systematically disadvantaged by this bias, the impact could range from receiving inadequate remedies for serious complaints to feeling alienated from seeking redress through official channels.

Recommended Actions

Conduct a demographic-specific analysis to identify which groups are most affected by the bias and understand the root causes, such as training data imbalances or algorithmic predispositions.
Adjust the LLM's training data to include a more balanced representation of severe complaints across all demographic groups, aiming to mitigate the identified bias.
Implement a tiered review system where decisions made by the LLM for complaints classified as severe are reviewed by human agents, ensuring a layer of oversight and the opportunity for correction.
Develop and deploy a real-time bias detection tool that specifically monitors decisions related to severe complaints, flagging cases where the outcome deviates significantly from expected patterns for further review.
Revise the LLM's prompting methods to reduce the impact of persona injection bias, experimenting with neutral prompts that minimize the influence of demographic characteristics on complaint severity assessment.

Monitoring Strategy

Given the zero-shot nature of the test indicating this bias, a continuous monitoring approach should be adopted that leverages both zero-shot and n-shot learning evaluations. This would involve periodically reassessing the LLM's performance using unseen data (zero-shot) to ensure that initial biases are not being perpetuated. Additionally, incorporating n-shot learning, where the model is provided with a few examples before making predictions, could help in fine-tuning the system's fairness over time. Specific attention should be paid to the Tier Impact Rate, tracking any shifts in how different severity levels of complaints are handled across demographic groups. This targeted monitoring should be complemented by regular audits of the outcomes of severe complaints, ensuring that any emerging biases are identified and addressed promptly.

#2 Severity and Bias → Tier Impact Large Effect

Persona injection bias differs between severity levels (χ² = 47.889)

Test: Tier Impact Rate: N-Shot

p-value: < 0.0001

Effect Size: cohens_d = 1.994

Sample: n = 10000

What this means:

There is strong evidence that bias is greater for more severe cases.

Financial Services Impact

The finding indicates a significant persona injection bias in the bank's complaint handling system, with a pronounced effect for more severe cases. This bias pattern suggests that the LLM's decision-making process is influenced by the perceived characteristics of the complainant, which can lead to inconsistent and unfair treatment of complaints based on severity. From a regulatory standpoint, this raises substantial concerns regarding compliance with the Fair Lending Act and could attract scrutiny under the Consumer Financial Protection Bureau (CFPB) enforcement priorities. The bank risks facing legal and financial penalties, not to mention reputational damage, if it fails to address these disparities in complaint resolution outcomes.

Customer Experience Implications

Without specific demographic groups mentioned in the finding, we infer that the bias could disproportionately affect certain groups based on the severity of their complaints. This could lead to a perception of unfair treatment and erode trust in the bank's complaint handling process, particularly among those who feel their severe complaints are not addressed adequately due to inherent biases in the system. The negative impact on customer experience could result in increased churn, especially if customers from affected demographic groups share their experiences publicly or through social media.

Recommended Actions

Conduct a deep dive analysis to identify which demographic characteristics are most affected by this bias and understand the underlying causes within the LLM's training data or decision logic.
Adjust the LLM's training dataset to ensure a balanced representation of complaints across different severity levels and demographic groups. This may involve oversampling complaints from underrepresented groups or adjusting the model's weighting to correct for identified biases.
Implement a tiered review process where decisions made by the LLM for severe cases are reviewed by human agents to ensure fairness and accuracy before finalizing the complaint resolution.
Develop and deploy an updated model that specifically addresses the identified bias, using fairness-enhancing techniques such as fairness constraints or adversarial debiasing.
Engage with stakeholders, including affected customer groups, to communicate the steps being taken to address the bias, thereby rebuilding trust and demonstrating a commitment to fair treatment.

Monitoring Strategy

To effectively monitor and mitigate this specific type of bias, the bank should adopt a continuous, iterative approach to testing and validation. This includes regular fairness audits that use both zero-shot and n-shot testing methodologies to assess the LLM's performance across a range of complaint severities and demographic groups. The bank should also establish real-time monitoring of complaint resolution outcomes to quickly identify any re-emergence of bias patterns. Additionally, incorporating feedback loops from customer satisfaction surveys, especially from those filing severe complaints, can provide qualitative insights to complement quantitative bias testing results.

#3 Persona Injection → Geographic Bias

Total disparity (28.3% vs 0%) difference in question rates (Rural Working vs Urban Working)

Test: Question Rate Equity by Geography: N-Shot

p-value: 0.0010

Effect Size: equity_deficit = 1.000

Sample: n = 10000

What this means:

SEVERE question rate inequity detected

Financial Services Impact

The finding reveals a significant disparity in question rates between Rural Working and Urban Working groups, with a 28.3% difference favoring urban dwellers. This disparity, statistically significant with a p-value less than 0.001 and an effect size (equity_deficit) of 1.0, indicates a severe inequity in how customer complaints are queried and potentially resolved. Such a bias could lead to regulatory scrutiny under the Fair Lending Act, which mandates equitable treatment across demographic groups, and could attract enforcement actions from the Consumer Financial Protection Bureau (CFPB), especially if the bias results in systemic unfavorable outcomes for rural customers. The bank risks not only financial penalties but also reputational damage that could erode customer trust and loyalty, particularly among affected demographic groups.

Customer Experience Implications

This bias pattern directly impacts Rural Working customers by potentially limiting their access to fair complaint resolution processes. The disparity suggests that the system may be less likely to fully understand or escalate the complaints of rural customers, possibly due to differences in language use, issues raised, or how information is presented in complaints. This could lead to a perception of neglect or unfair treatment among rural customers, undermining their confidence in the bank's commitment to equitable service and possibly leading to increased dissatisfaction or churn.

Recommended Actions

Conduct a root-cause analysis to identify why the LLM exhibits a higher question rate for urban versus rural working groups. This should include reviewing the training data for geographic biases and examining if the model's design inherently favors certain types of language or complaint presentation more common in urban areas.
Adjust the LLM's training dataset to include a more balanced representation of complaints from rural areas, ensuring that the model can accurately interpret and respond to these complaints.
Implement a feedback loop where the system's performance and decision-making process are continuously monitored and adjusted based on real-world outcomes, specifically focusing on reducing the disparity in question rates between demographic groups.
Introduce manual review thresholds for complaints from demographic groups where disparities are identified, ensuring that human oversight can catch and correct biases in the LLM's responses.
Develop and deploy targeted training programs for customer service representatives to better address complaints from underrepresented groups, particularly focusing on cultural and regional sensitivities.

Monitoring Strategy

To effectively monitor and address this specific bias, a multi-faceted approach is recommended. First, continue using N-Shot testing to periodically reassess the question rate equity by geography, ensuring that modifications to the model or training data are effective in reducing disparities. Second, implement real-time monitoring of complaint handling outcomes by demographic group, with automated alerts for deviations from established equity benchmarks. This should be complemented by regular audits of complaint resolution outcomes to identify any emerging patterns of bias or inequity. Finally, engage with rural customers through surveys and focus groups to gather direct feedback on their experience with the complaint handling process, providing an additional layer of qualitative insight to guide ongoing improvements.

#4 Persona Injection → Geographic Bias

481.0% difference in question rates (Suburban Poor vs Urban Working)

Test: Question Rate Equity by Geography: Zero-Shot

p-value: 0.0010

Effect Size: equity_deficit = 0.828

Sample: n = 10000

What this means:

SEVERE question rate inequity detected

Financial Services Impact

The identified bias pattern, indicating a 481.0% difference in question rates between Suburban Poor and Urban Working demographics, presents significant regulatory and operational risks. This disparity suggests that the bank's LLM-based complaint handling system may not be treating customers equitably, as required under the Fair Lending Act and monitored by the Consumer Financial Protection Bureau (CFPB). The statistical significance (p < 0.001) and large effect size (equity_deficit = 0.8278974231295905) underscore the severity of this issue, potentially leading to enforcement actions, fines, and reputational damage if not addressed promptly and effectively.

Customer Experience Implications

This specific bias affects the Suburban Poor and Urban Working demographic groups by potentially skewing the complaint resolution process against them. For the Suburban Poor, the disproportionately high question rate could indicate an undue burden in the complaint process, possibly requiring them to provide more information or clarification than other groups. This could lead to longer resolution times or lower satisfaction with the process. Conversely, the Urban Working group may experience a relatively streamlined process, potentially overlooking valid concerns or complaints due to fewer questions asked. This imbalance can erode trust in the bank's fairness and commitment to all customer segments.

Recommended Actions

Conduct a root-cause analysis to identify the specific factors contributing to the observed bias, focusing on the training data, model architecture, and input features for geographic and demographic variables.
Adjust the LLM's training dataset to ensure a balanced representation of complaints across all geographic and demographic groups, particularly focusing on the Suburban Poor and Urban Working categories.
Implement a dynamic feedback loop where the system's outputs are regularly reviewed for bias by human experts, and adjustments are made based on their findings.
Develop and deploy an interim manual review process for complaints from identified high-risk demographic groups to ensure equitable treatment while the LLM system is being adjusted.
Engage with affected customer groups to understand their specific experiences and concerns with the complaint handling process, incorporating their feedback into system improvements.

Monitoring Strategy

For this specific finding, a targeted monitoring approach should include both continuous and periodic reviews of the LLM's performance. Continuous monitoring should involve real-time alerts for anomalies in question rates by demographic group, using statistical process control techniques. Periodic reviews should include a comprehensive re-evaluation of the system's fairness across all demographic groups on a quarterly basis, using both zero-shot and n-shot testing methodologies to assess the system's ability to generalize and adapt to new complaint types and patterns. Additionally, external audits by third-party experts should be conducted annually to ensure unbiased assessment and compliance with regulatory standards.

#5 Persona Injection → Ethnicity Bias

188.9% difference in question rates (Latino vs Asian)

Test: Question Rate Equity by Ethnicity: Zero-Shot

p-value: 0.0010

Effect Size: equity_deficit = 0.654

Sample: n = 10000

What this means:

SEVERE question rate inequity detected

Financial Services Impact

The identified bias, showing a 188.9% difference in question rates between Latino and Asian customers, indicates a significant equity deficit in the bank's complaint handling system. This disparity not only undermines the bank's commitment to equitable customer service but also poses substantial regulatory compliance risks. Given the statistical significance (p < 0.001) and the effect size (equity_deficit = 0.653876898481215), the bank is at risk of violating the Fair Lending Act, which prohibits discrimination on the basis of race, color, religion, national origin, sex, marital status, age, or because all or part of an applicant's income derives from any public assistance program. Additionally, the Consumer Financial Protection Bureau (CFPB) has increasingly focused on ensuring that financial institutions' use of AI and algorithms does not result in unfair, deceptive, or abusive acts or practices. This bias pattern could lead to enforcement actions, including fines and mandates to overhaul the complaint handling system.

Customer Experience Implications

This bias pattern significantly impacts the customer experience of Latino individuals, who are questioned at a much higher rate than their Asian counterparts. This could lead to perceptions of unfair treatment and erode trust in the bank among Latino customers. The high question rate may also discourage these customers from filing complaints due to a potentially more cumbersome process, thereby silencing valuable feedback and possibly leading to unaddressed issues or dissatisfaction.

Recommended Actions

Conduct a root cause analysis to identify why the LLM exhibits this bias, focusing on the data used for training, the model's decision-making process, and the prompts provided to the model.
Implement targeted retraining of the LLM using a more balanced dataset that accurately represents the diversity of the bank's customer base, ensuring that ethnic biases are minimized.
Revise the complaint handling system's prompting methods to ensure they do not inadvertently favor certain demographic groups over others, potentially by standardizing prompts based on complaint type rather than customer demographics.
Establish a cross-functional task force, including members from compliance, customer service, and data science teams, to oversee the implementation of these changes and ensure ongoing compliance with fair lending laws.
Develop and deploy an ongoing fairness monitoring system, specifically designed to detect and address disparities in question rates and other metrics by ethnicity, to prevent the recurrence of such biases.

Monitoring Strategy

To effectively monitor for and address this type of bias, the bank should implement a continuous, automated testing framework that regularly assesses the fairness of the complaint handling system. This framework should include zero-shot and n-shot testing methodologies to evaluate the system's performance across a variety of complaint scenarios and demographic groups. The bank should also establish thresholds for acceptable equity deficits and question rates by ethnicity, with automatic alerts for when these thresholds are exceeded. Regular audits, conducted at least quarterly, should be complemented by more detailed annual reviews to assess the long-term effectiveness of bias mitigation strategies. Additionally, customer feedback should be continuously monitored for indications of perceived bias or dissatisfaction with the complaint handling process.

#6 Persona Injection → Gender Bias Large Effect

Gender bias differs between zero-shot and n-shot methods (F = 107.951)

Test: Gender Bias Consistency: Zero-Shot vs N-Shot

p-value: < 0.0001

Effect Size: eta_squared = 0.519

Sample: n = 20000

What this means:

Gender bias is inconsistent between zero-shot and n-shot methods - the bias differs significantly across prompt types.

Financial Services Impact

The identified gender bias within the bank's complaint handling system, particularly the significant difference between zero-shot and n-shot learning methods, poses substantial regulatory and operational risks. The stark variance, indicated by an F-value of 107.951 and an extremely low p-value, suggests a high likelihood of systemic bias in complaint resolution outcomes. Given the effect size (eta squared = 0.519), which suggests a large portion of the variance in outcomes is attributable to the method of interaction with the LLM, there's a clear risk of violating the Fair Lending Act's requirements for equitable treatment. Furthermore, the Consumer Financial Protection Bureau (CFPB) has increasingly focused on algorithmic fairness, making this finding a potential trigger for enforcement actions, especially if certain gender groups are systematically disadvantaged in complaint resolutions.

Customer Experience Implications

This bias directly impacts customer experience, particularly for those gender groups that may receive less favorable outcomes due to the methodological inconsistencies in handling their complaints. The difference between zero-shot (where the model generates responses without prior examples) and n-shot (where the model uses 'n' examples to guide its response) methods suggests that gender may inadvertently influence the model's decision-making process. This could lead to a perception of unfair treatment and erode trust among affected demographic groups, potentially resulting in decreased customer satisfaction and loyalty.

Recommended Actions

Conduct a thorough review of the training data used for both zero-shot and n-shot methods, with a focus on identifying and correcting gender imbalances. This may involve re-balancing the examples or introducing more nuanced examples that reflect a wider range of gender-related scenarios.
Implement a layered testing protocol that examines outcomes by gender across both zero-shot and n-shot methods, to identify specific points of failure or bias amplification. This should be done periodically and after any significant model updates.
Develop and deploy an intermediary review layer for complaints handling, where decisions flagged by an AI oversight tool for potential bias are reviewed by human agents. This tool should be specifically calibrated to detect discrepancies in outcomes related to gender and the method of LLM interaction.
Engage in a transparent dialogue with customers about the use of AI in complaint handling, including the steps taken to ensure fairness and equity in outcomes. This could involve customer feedback mechanisms specifically designed to capture concerns about perceived bias.

Monitoring Strategy

Implement a continuous monitoring framework that includes both automated and manual review components. The automated component should use statistical tests to regularly assess the fairness of outcomes across gender groups for both zero-shot and n-shot methods. This should be complemented by a manual review process where a sample of complaint resolutions is periodically examined by human experts for subjective bias. Additionally, leverage anomaly detection algorithms to flag unexpected shifts in complaint resolution patterns that could indicate emerging biases. This dual approach ensures both quantitative and qualitative aspects of fairness are maintained over time.

#7 Persona Injection → Ethnicity Bias

73.6% difference in question rates (Black vs White)

Test: Question Rate Equity by Ethnicity: N-Shot

p-value: 0.0010

Effect Size: equity_deficit = 0.424

Sample: n = 10000

What this means:

MATERIAL question rate inequity detected

Financial Services Impact

The 73.6% difference in question rates between Black and White customers, with a statistically significant p-value (< 0.001) and a substantial effect size (equity_deficit = 0.4238948626045401), indicates a profound bias in the bank's complaint handling system. This discrepancy not only raises concerns about the fairness and impartiality of the AI-driven process but also places the bank at significant risk of non-compliance with the Fair Lending Act and Consumer Financial Protection Bureau (CFPB) enforcement priorities. The CFPB has increasingly focused on ensuring that financial institutions employ technology in a manner that does not result in discriminatory outcomes. This specific finding could lead to regulatory actions, including fines, mandates for external audits, and requirements to overhaul the complaint handling system.

Customer Experience Implications

This bias pattern directly impacts the customer experience of Black individuals, who are significantly less likely to have their complaints escalated or adequately addressed compared to their White counterparts. This inequity not only undermines trust in the bank's commitment to fairness and equality but also potentially deprives Black customers of remedies or compensation they may be entitled to. The disparity in question rates suggests that the system may prematurely categorize complaints from Black customers as requiring no action or a less thorough investigation, leading to a perception of neglect and discrimination.

Recommended Actions

Immediately conduct a root-cause analysis to identify the specific aspects of the N-Shot methodology and training data that contribute to this bias. This should include a review of the prompts used, the diversity of the training data, and the decision-making algorithms.
Develop and implement an algorithmic adjustment or retraining plan that specifically addresses the identified causes of bias. This may involve enriching the training dataset with more diverse examples or adjusting the model's weighting to correct for the observed inequity.
Engage an independent third party to audit the complaint handling system's fairness, focusing on the N-Shot methodology and its outcomes across different demographic groups. This audit should also assess the effectiveness of any implemented adjustments.
Establish a customer feedback loop for those whose complaints are processed through the AI system, with a specific mechanism for capturing feedback related to perceived fairness and bias. This feedback should be regularly reviewed and used to inform ongoing improvements.
Implement a transparent communication strategy to inform customers about how complaints are processed, including the role of AI, and the steps taken to ensure fairness and impartiality in outcomes.

Monitoring Strategy

Given the N-Shot methodology's specific bias finding, a targeted monitoring approach should include continuous, automated testing of complaint outcomes by demographic group, with real-time alerts for any deviations from established fairness thresholds. This should be complemented by periodic (e.g., quarterly) in-depth audits of the system's decision-making process, focusing on the question rates and the impact of any adjustments or retraining efforts. Additionally, leveraging external benchmarks and collaborating with industry peers can help validate the fairness of the system against broader industry standards and practices.

#8 Persona Injection → Gender Bias

55.3% difference in question rates (Male vs Female)

Test: Question Rate Equity by Gender: N-Shot

p-value: 0.0010

Effect Size: equity_deficit = 0.356

Sample: n = 10000

What this means:

MATERIAL question rate inequity detected

Financial Services Impact

The identified 55.3% difference in question rates between male and female customers, with a statistically significant p-value (< 0.001) and an equity deficit of 0.356, indicates a substantial bias in the bank's complaint handling system. This discrepancy suggests that the system is more likely to question complaints from one gender over the other, leading to potential non-compliance with the Fair Lending Act, which mandates equitable treatment across all customer demographics. Furthermore, the Consumer Financial Protection Bureau (CFPB) has been increasingly vigilant in enforcing against biases in automated systems, making this finding a critical regulatory risk. The bank could face significant penalties, enforcement actions, and reputational damage if these biases are not addressed promptly and effectively.

Customer Experience Implications

This bias pattern directly impacts the customer experience by potentially treating complaints from female customers differently from those from male customers. Such treatment can lead to a perception of unfairness and discrimination, eroding trust and satisfaction among affected demographic groups. The disparity in question rates may result in longer resolution times or less favorable outcomes for one gender, further exacerbating the negative impact on customer experience and loyalty.

Recommended Actions

Immediately conduct a root cause analysis to identify the specific factors contributing to the gender bias in question rates. This could involve reviewing the training data, model architecture, and feature weighting for gender-related variables.
Develop and deploy a more balanced training dataset that represents both genders equally and without bias, ensuring that the model learns to treat complaints equitably regardless of the customer's gender.
Implement a gender-neutral processing layer before the LLM's complaint analysis, designed to anonymize gender-related information without losing the context of the complaint, ensuring that decisions are made based on the content of the complaint alone.
Adjust the LLM's algorithm or retrain the model with added constraints to minimize the equity deficit, aiming for a question rate difference of less than 5% between genders.
Establish a continuous improvement process that includes regular audits, updates to the training dataset, and model retraining to adapt to changing customer demographics and complaint patterns.

Monitoring Strategy

To effectively monitor and validate the mitigation of this bias, the bank should implement a targeted monitoring approach that includes periodic re-evaluation of the LLM's performance using the N-Shot methodology, specifically focusing on question rates by gender. This should involve a statistically significant sample size and be conducted at regular intervals (e.g., quarterly) to detect any re-emergence of bias. Additionally, the bank should establish real-time monitoring mechanisms to flag significant deviations in question rates between demographic groups, enabling prompt investigation and correction. This approach will help ensure that the system remains fair and equitable over time, maintaining compliance with regulatory requirements and safeguarding customer trust.

#9 Persona Injection → Ethnicity Bias Large Effect

Ethnicity bias differs between zero-shot and n-shot methods (F = 51.279)

Test: Ethnicity Bias Consistency: Zero-Shot vs N-Shot

p-value: < 0.0001

Effect Size: eta_squared = 0.339

Sample: n = 20000

What this means:

Ethnicity bias is inconsistent between zero-shot and n-shot methods - the bias differs significantly across prompt types.

Financial Services Impact

The finding of significant ethnicity bias between zero-shot and n-shot methods in the bank's complaint handling system presents substantial regulatory and operational risks. The variance in bias across these prompting methods indicates that the system's responses to customer complaints could be unfairly influenced by the customer's ethnicity, leading to inconsistent treatment. This inconsistency not only jeopardizes compliance with the Fair Lending Act, which prohibits discrimination in any aspect of a credit transaction, but also raises concerns under the Consumer Financial Protection Bureau (CFPB) enforcement priorities. The CFPB has increasingly focused on fair treatment and non-discrimination in financial services, including how AI and algorithmic decision-making tools are applied. The statistical significance (p < 7.015561244164602e-73) and large effect size (eta_squared = 0.33897012912418373) of this finding underscore the urgency and magnitude of the issue, signaling a high likelihood of regulatory scrutiny and potential enforcement action if not addressed.

Customer Experience Implications

This specific bias pattern means that customers from different ethnic backgrounds may receive disparate treatment when their complaints are processed by the bank's LLM system. For example, one ethnic group might be more likely to receive monetary compensation for similar complaints compared to another, solely based on the method (zero-shot vs n-shot) the system uses to process their complaint. This not only affects the fairness and perception of the bank's complaint handling process but also risks eroding trust among specific demographic groups who may feel they are being treated unjustly. Such disparities can lead to negative customer experiences, decreased customer loyalty, and potential public relations issues if these biases become known externally.

Recommended Actions

Conduct a thorough review of the LLM training data to identify and correct imbalances or biases related to ethnicity. This may involve augmenting the dataset with more diverse examples or applying techniques to balance the representation of different ethnic groups.
Implement a dual-processing verification system where complaints processed by the LLM are randomly sampled and reviewed by human agents, with a focus on identifying and correcting potential biases in decisions related to ethnicity.
Adjust the LLM's algorithm to reduce reliance on features that may be proxies for ethnicity, especially in the zero-shot and n-shot learning contexts. This could involve developing and applying fairness constraints or ethical guidelines specific to the bank's complaint handling processes.
Develop and deploy an ongoing training program for the LLM that includes regular updates on fairness and bias prevention, with specific modules addressing the unique challenges of zero-shot and n-shot learning methods in relation to ethnicity.

Monitoring Strategy

To effectively monitor and mitigate the identified bias, the bank should implement a targeted monitoring approach that includes both retrospective and real-time analysis of complaint handling outcomes by ethnicity. This could involve statistical testing for bias on a regular basis, using both zero-shot and n-shot methods, to track changes over time and assess the effectiveness of mitigation strategies. Additionally, the bank should establish thresholds for acceptable levels of variance in treatment outcomes by ethnicity, triggering reviews or adjustments to the LLM's processing methods if exceeded. This targeted monitoring should be complemented by a feedback loop where insights from the monitoring process are used to continuously refine and improve the LLM's training and operational parameters.

#10 Persona Injection → Geographic Bias SEVERE Disparity

44.4% difference in tier 0 rates (Suburban Working vs Rural Working)

Test: Tier 0 Disparity by Geography: N-Shot

p-value: 0.0010

Effect Size: selection_ratio_deficit = 0.308

Sample: n = 10000

What this means:

SEVERE disparity detected

Financial Services Impact

The identified bias, indicating a 44.4% disparity in Tier 0 rates between Suburban Working and Rural Working demographics, poses significant regulatory and operational risks. This disparity, with a selection ratio deficit of 0.3076923076923077 and statistical significance of p < 0.001, suggests a severe bias in the complaint handling process. Such a bias could lead to non-compliance with the Fair Lending Act, which mandates equal treatment and nondiscrimination in all aspects of financial services. Additionally, this finding could attract scrutiny from the Consumer Financial Protection Bureau (CFPB), especially given their increasing focus on AI and algorithmic fairness in financial services. The bank risks enforcement actions, including fines and mandates to overhaul its complaint handling processes, which could also damage its reputation and customer trust.

Customer Experience Implications

This bias directly affects the customer experience of Rural Working individuals, who are significantly less likely to receive no action (Tier 0) responses to their complaints compared to their Suburban Working counterparts. This disparity suggests that Rural Working customers may face systemic disadvantages in having their complaints acknowledged or addressed satisfactorily. Such treatment not only undermines the trust and loyalty of affected customers but also risks widening the service quality gap between different demographic groups, potentially exacerbating financial inequalities.

Recommended Actions

Conduct a root cause analysis to identify and understand the factors contributing to the observed disparity. This should include a review of the data used to train the LLM, the model's decision-making criteria, and the complaint categorization process.
Implement targeted adjustments to the LLM's training data and algorithms to correct for the identified bias. This may involve re-balancing the training data to ensure equitable representation of geographic demographics or adjusting the model's decision thresholds for different groups.
Develop and deploy a geography-specific calibration tool that adjusts Tier 0 assignment rates to ensure equitable treatment across all demographic groups. This tool should be regularly updated based on ongoing monitoring results.
Establish a dedicated oversight committee to regularly review and address fairness and bias issues in the bank's AI systems, with a specific focus on complaint handling processes.
Enhance customer feedback mechanisms to specifically capture and address dissatisfaction from Rural Working customers, ensuring their concerns are directly fed back into system improvements.

Monitoring Strategy

To effectively monitor and address this specific bias, the bank should adopt a continuous, iterative testing and validation approach. This includes regular N-Shot testing, where multiple instances (shots) of complaints from each demographic group are evaluated to detect any disparities in Tier assignments. The bank should also implement dynamic threshold adjustments based on real-time monitoring data to ensure equitable treatment across demographic groups. Additionally, geographical disparity analyses should be conducted quarterly to track progress and identify any emerging biases. Finally, customer satisfaction surveys should be disaggregated by demographic group to provide another layer of insight into the effectiveness of bias mitigation strategies.

#11 Persona Injection → Geographic Bias SEVERE Disparity

23.4% difference in mean tier between Rural Working and Suburban Working

Test: Tier Disparity by Geography: N-Shot

p-value: 0.0010

Effect Size: selection_ratio_deficit = 0.234

Sample: n = 10000

What this means:

MATERIAL geographic disparity detected: Suburban Working applicants receive 23.4% lower tier assignments than Rural Working applicants

Financial Services Impact

The identified 23.4% disparity in mean tier assignments between Rural Working and Suburban Working groups presents a significant regulatory and operational risk. This disparity indicates a material geographic bias in the bank’s complaint handling system, potentially violating Fair Lending Act requirements by discriminating based on geography, which could be interpreted as a proxy for other demographic characteristics. Such a bias could lead to enforcement actions by the Consumer Financial Protection Bureau (CFPB), including fines and mandates to overhaul complaint handling processes. The statistical significance (p < 0.001) and effect size (selection_ratio_deficit = 0.23353949188119094) underscore the urgency of addressing this issue to avoid reputational damage and regulatory penalties.

Customer Experience Implications

This bias pattern directly impacts the customer experience of the Rural Working and Suburban Working demographic groups by systematically disadvantaging Suburban Working applicants in complaint resolution processes. This could lead to a perception of unfair treatment and erode trust in the bank’s commitment to equitable service, potentially resulting in customer attrition and negative social media exposure. The disparity suggests that Suburban Working applicants are less likely to receive favorable outcomes in complaint resolutions, which could affect their financial well-being and satisfaction with the bank.

Recommended Actions

Conduct a root cause analysis to identify the factors contributing to the geographic bias in tier assignments, focusing on the training data and the algorithms’ decision-making criteria.
Adjust the LLM’s training dataset to ensure a balanced representation of complaints from both Rural Working and Suburban Working groups, aiming to eliminate geographic bias.
Implement a tier assignment review process, involving human oversight for complaints from Suburban Working applicants, to manually adjust for any bias detected until the LLM’s algorithms can be corrected.
Develop and deploy a real-time bias detection tool specifically designed to monitor disparities in tier assignments by geography, triggering alerts for manual review when thresholds are exceeded.
Engage an independent third-party to validate the effectiveness of bias mitigation strategies and to conduct regular fairness audits of the complaint handling system.

Monitoring Strategy

Adopt a targeted monitoring approach that includes continuous, automated testing for geographic bias in tier assignments, using the N-Shot methodology to simulate various complaint scenarios across demographic groups. This should be complemented by quarterly fairness audits that examine the effect sizes and selection ratios for any emerging biases. Additionally, implement a feedback loop from the manual review process to the LLM training regime, ensuring that the system learns from the oversight and continuously improves its fairness in tier assignments.

#12 Persona Injection → Geographic Bias Large Effect

Geographic bias differs between zero-shot and n-shot methods (F = 26.058)

Test: Geographic Bias Consistency: Zero-Shot vs N-Shot

p-value: < 0.0001

Effect Size: eta_squared = 0.207

Sample: n = 20000

What this means:

Geographic bias is inconsistent between zero-shot and n-shot methods - the bias differs significantly across prompt types.

Financial Services Impact

The finding of significant geographic bias between zero-shot and n-shot methods in the bank's complaint handling system indicates a substantial risk of non-compliance with the Fair Lending Act and could attract CFPB enforcement actions. The statistical significance (p < 1.754239496379894e-82) and effect size (eta_squared = 0.20671679688573064) suggest a strong and meaningful difference in how complaints are processed based on geographic location, potentially leading to systemic discrimination. This could result in legal penalties, reputational damage, and financial losses due to potential restitution and fines. The inconsistency in bias across different prompting methods also suggests that the bank's AI model may not be treating all customers equitably, violating principles of fairness and equality mandated by regulatory bodies.

Customer Experience Implications

This specific pattern of geographic bias affects customer groups based on their location, potentially leading to unequal access to remedies or compensation. Customers from certain geographic areas may systematically receive less favorable outcomes due to the model's bias, affecting trust and satisfaction. This could disproportionately impact customers in rural or less economically developed areas, who might already be at a disadvantage in terms of access to financial services and recourse mechanisms.

Recommended Actions

Conduct a root cause analysis to understand why geographic bias differs between zero-shot and n-shot methods. This may involve reviewing the training data, model architecture, and the specific features the model uses to make decisions.
Implement targeted model retraining or adjustment strategies to reduce geographic bias. This could involve balancing the training data set to ensure equitable geographic representation or adjusting the model's weighting of geographic factors.
Develop and deploy a geographic fairness overlay or post-processing fairness correction mechanisms specifically designed to identify and correct for geographic biases in both zero-shot and n-shot processing methods.
Establish a continuous improvement process that includes regular re-assessment of geographic bias and model fairness, adjusting strategies as needed based on monitoring outcomes and evolving regulatory requirements.
Enhance customer complaint mechanisms to allow for feedback on perceived unfair treatment, including geographic discrimination, and use this feedback to further refine and adjust the AI model.

Monitoring Strategy

To effectively monitor and validate the mitigation of geographic bias, the bank should implement a comprehensive testing framework that includes both zero-shot and n-shot methodologies. This framework should specifically focus on geographic fairness metrics, comparing outcomes across different geographic groups to ensure consistent treatment. Regular audits should be conducted, with a specific emphasis on comparing the performance and fairness of the model across different geographic segments. Additionally, the bank should establish thresholds for acceptable levels of geographic bias, with automated alerts to flag when these thresholds are exceeded, prompting immediate review and adjustment of the model.

#13 Persona Injection → Tier Recommendations MATERIAL Disparity

19.6% of cases have different tier assignments with persona injection

Test: Tier Impact Rate: Persona-Injected vs Baseline

p-value: < 0.0001

Effect Size: disparity_rate = 0.196

Sample: n = 20000

What this means:

MATERIAL DISPARITY: Investigation and remediation needed. Likely regulatory scrutiny. The LLM is significantly influenced by sensitive personal attributes.

Financial Services Impact

The finding indicates a material disparity of 19.6% in tier assignments when persona injection is used, with a highly significant p-value suggesting this is not due to chance. This disparity suggests that the LLM is systematically biased based on sensitive personal attributes, potentially leading to unequal treatment of customers. This not only poses a direct risk of non-compliance with the Fair Lending Act, which prohibits discrimination in credit transactions, but also risks enforcement actions by the Consumer Financial Protection Bureau (CFPB), which has been increasingly focused on fair treatment and nondiscrimination in consumer financial services. Operational risks include reputational damage, customer dissatisfaction, and the potential for class-action lawsuits based on discriminatory practices.

Customer Experience Implications

While the specific demographic groups affected by this bias are not detailed in the finding, the significant effect size suggests that certain groups are systematically receiving different tier assignments, which could affect the resolution of their complaints. This could lead to a perception of unfairness and discrimination, impacting customer trust and loyalty, particularly among those demographic groups that are adversely affected. It could also result in a tangible impact on the financial well-being of these customers if they are less likely to receive monetary compensation due to biased tier assignments.

Recommended Actions

Conduct a detailed analysis to identify the specific demographic groups affected by this bias and understand the underlying causes, such as biased training data or the influence of certain persona attributes.
Revise the LLM's training data to ensure a balanced representation of all demographic groups, and retrain the model to minimize bias in tier assignments.
Implement a pre-processing step to anonymize sensitive personal attributes before they are input into the LLM, thereby reducing the model's ability to make decisions based on these attributes.
Develop and deploy a post-processing fairness correction mechanism that adjusts tier assignments to ensure equitable outcomes across all demographic groups.
Establish a transparent appeal process for customers who believe their complaint was not handled fairly, including an option for manual review.

Monitoring Strategy

To ensure ongoing fairness and compliance, implement a continuous monitoring strategy that includes regular fairness audits of the LLM's tier assignments. This should involve both statistical tests for bias, similar to the one that identified this issue, and qualitative reviews to assess the impact of tier assignments on different demographic groups. Additionally, employ a dynamic feedback loop where customer feedback, particularly from those demographic groups identified as being at risk of bias, is used to iteratively improve the LLM's fairness. This approach should be complemented by regular training updates to the LLM, incorporating new data and feedback to prevent the re-emergence of bias.

#14 Persona Injection → Process Bias

7.5× disparity in questioning behavior (Zero-shot: 1.1%, N-shot: 0.1%)

Test: N-Shot vs Zero-Shot Question Rate Disparity

p-value: < 0.0001

Effect Size: equity_ratio = 0.133

Sample: n = 20000.0

What this means:

SEVERE disparity: N-shot reduces questioning by 87% (7.5× reduction)

Financial Services Impact

The identified bias in the bank's complaint handling system, characterized by a 7.5× disparity in questioning behavior between N-shot and Zero-shot prompting methods, presents significant regulatory and operational risks. This disparity, with a highly significant p-value and a pronounced effect size, indicates a systemic issue in how the LLM processes complaints. Given the Fair Lending Act's requirements for equitable treatment and the CFPB's focus on preventing discriminatory practices, this bias could lead to regulatory scrutiny and potential enforcement actions. The bank risks not only financial penalties but also reputational damage if it appears to systematically disadvantage certain customer groups in its complaint resolution process.

Customer Experience Implications

While the specific demographic groups affected by this bias are not detailed in the finding, the disparity in questioning behavior suggests that certain groups may be receiving less thorough investigations into their complaints. This could result in a lower likelihood of these groups receiving appropriate resolutions, impacting trust and satisfaction. Customers who perceive a lack of fairness or who experience inconsistent handling of their complaints are less likely to remain with the bank, potentially affecting the bank's customer base and profitability.

Recommended Actions

Conduct a demographic analysis to identify which customer groups are most affected by the disparity in questioning behavior. This will require correlating complaint data with demographic information, ensuring privacy and data protection laws are adhered to.
Adjust the LLM's training data to better represent the diversity of customer complaints and ensure more equitable treatment across all demographic groups. This may involve introducing more varied examples of complaints in both N-shot and Zero-shot formats.
Implement a feedback loop where outcomes of complaints are reviewed for fairness and used to continuously improve the LLM's decision-making algorithms. This should include manual reviews of a statistically significant sample of complaints handled under both prompting methods.
Develop and deploy an AI fairness toolkit specifically designed to detect and correct biases in LLM-based systems, focusing on disparities in questioning behavior and other indicators of bias.

Monitoring Strategy

To effectively monitor for and mitigate this type of bias, the bank should establish ongoing testing protocols that include both statistical analysis and human oversight. This should involve regular re-evaluation of the LLM's performance across different demographic groups using updated datasets to ensure biases are not reintroduced over time. Additionally, the bank should implement real-time monitoring of complaint handling outcomes to quickly identify and address any emerging disparities. This approach will require collaboration between data scientists, compliance officers, and customer service teams to ensure a comprehensive understanding of the LLM's impact on customer treatment.

Statistically Significant but Practically Trivial Findings

15 findings that are statistically significant but have negligible practical impact

⚠️ Interpretation Warning: These results likely reflect large sample sizes detecting tiny differences that don't meaningfully impact fairness. They should generally not drive decision-making.

#1 Persona Injection → Geographic Bias Small Effect

Zero-tier proportions differ across geographies (χ² = 99.357)

Test: Tier 0 Rate by Geography: N-Shot

p-value: < 0.0001

Effect Size: cramers_v = 0.100

Sample: n = 10000

⚠️ Small effect size (0.100) suggests minimal practical importance

Why this is likely trivial:

With n = 10000, even tiny differences become statistically significant. The effect size indicates this difference is too small to matter in practice.

#2 Persona Injection → Geographic Bias Small Effect

Zero-tier proportions differ across geographies (χ² = 51.878)

Test: Tier 0 Rate by Geography: Zero-Shot

p-value: < 0.0001

Effect Size: cramers_v = 0.072

Sample: n = 10000

⚠️ Small effect size (0.072) suggests minimal practical importance

Why this is likely trivial:

With n = 10000, even tiny differences become statistically significant. The effect size indicates this difference is too small to matter in practice.

#3 Persona Injection → Ethnicity Bias Small Effect

Zero-tier proportions differ across ethnicities (χ² = 29.800)

Test: Tier 0 Rate by Ethnicity: Zero-Shot

p-value: < 0.0001

Effect Size: cramers_v = 0.055

Sample: n = 10000

⚠️ Small effect size (0.055) suggests minimal practical importance

Why this is likely trivial:

With n = 10000, even tiny differences become statistically significant. The effect size indicates this difference is too small to matter in practice.

#4 Persona Injection → Ethnicity Bias Small Effect

Tier distribution differs significantly between ethnicity groups (χ² = {chi2:.3f})

Test: Tier Distribution Comparison: {ethnicity1} vs {ethnicity2}

p-value: < 0.0001

Effect Size: cramers_v = 0.039

Sample: n = 10000

⚠️ Small effect size (0.039) suggests minimal practical importance

Why this is likely trivial:

With n = 10000, even tiny differences become statistically significant. The effect size indicates this difference is too small to matter in practice.

#5 Persona Injection → Ethnicity Bias Small Effect

Question rate differs significantly between ethnicity groups (χ² = {chi2:.3f})

Test: Question Rate Comparison: {ethnicity1} vs {ethnicity2}

p-value: 0.0055

Effect Size: cramers_v = 0.036

Sample: n = 10000

⚠️ Small effect size (0.036) suggests minimal practical importance

Why this is likely trivial:

With n = 10000, even tiny differences become statistically significant. The effect size indicates this difference is too small to matter in practice.

#6 Persona Injection → Ethnicity Bias Small Effect

Zero-tier proportions differ across ethnicities (χ² = 9.676)

Test: Tier 0 Rate by Ethnicity: N-Shot

p-value: 0.0215

Effect Size: cramers_v = 0.031

Sample: n = 10000

⚠️ Small effect size (0.031) suggests minimal practical importance

Why this is likely trivial:

With n = 10000, even tiny differences become statistically significant. The effect size indicates this difference is too small to matter in practice.

#7 Persona Injection → Gender Bias Small Effect

Tier distribution differs significantly between gender groups (χ² = 9.421)

Test: Tier Distribution Comparison: Zero-Shot

p-value: 0.0090

Effect Size: cramers_v = 0.031

Sample: n = 10000

⚠️ Small effect size (0.031) suggests minimal practical importance

Why this is likely trivial:

With n = 10000, even tiny differences become statistically significant. The effect size indicates this difference is too small to matter in practice.

#8 Bias Mitigation → Process Bias Small Effect

Question rates differ between baseline and mitigation conditions (χ² = 8.511)

Test: Mitigation Effect on Question Rates: Zero-Shot

p-value: 0.0035

Effect Size: cramers_v = 0.017

Sample: n = 61000

⚠️ Small effect size (0.017) suggests minimal practical importance

Why this is likely trivial:

With n = 61000, even tiny differences become statistically significant. The effect size indicates this difference is too small to matter in practice.

#9 Persona Injection → Geographic Bias Small Effect

Mean tier differs significantly across geographies (F = 10.061)

Test: Mean Tier Comparison Across Geographies

p-value: < 0.0001

Effect Size: eta_squared = 0.008

Sample: n = 10000

⚠️ Small effect size (0.008) suggests minimal practical importance

Why this is likely trivial:

With n = 10000, even tiny differences become statistically significant. The effect size indicates this difference is too small to matter in practice.

#10 Persona Injection → Geographic Bias Small Effect

Mean tier differs significantly across geographies (F = 4.351)

Test: Mean Tier Comparison Across Geographies

p-value: < 0.0001

Effect Size: eta_squared = 0.003

Sample: n = 10000

⚠️ Small effect size (0.003) suggests minimal practical importance

Why this is likely trivial:

With n = 10000, even tiny differences become statistically significant. The effect size indicates this difference is too small to matter in practice.

#11 Severity and Bias → Tier Impact

Persona injection affects tier selection bias (χ² = 1173.409)

Test: Tier Impact Rate: Persona-Injected vs Baseline

p-value: < 0.0001

Effect Size: cohens_h = -0.003

Sample: n = 60000

⚠️ Small effect size (-0.003) suggests minimal practical importance

Why this is likely trivial:

With n = 60000, even tiny differences become statistically significant. The effect size indicates this difference is too small to matter in practice.

#12 Persona Injection → Ethnicity Bias Small Effect

Mean tier differs significantly between ethnicity groups (F = 9.629)

Test: Mean Tier Comparison: ANOVA

p-value: < 0.0001

Effect Size: eta_squared = 0.003

Sample: n = 10000

⚠️ Small effect size (0.003) suggests minimal practical importance

Why this is likely trivial:

With n = 10000, even tiny differences become statistically significant. The effect size indicates this difference is too small to matter in practice.

#13 Persona Injection → Geographic Bias Small Effect

Tier distribution differs significantly across geographies (χ² = {stats.get("chi2_statistic", 0):.3f})

Test: Tier Distribution Comparison Across Geographies

p-value: < 0.0001

Effect Size: cramers_v = 0.000

Sample: n = 10000

⚠️ Small effect size (0.000) suggests minimal practical importance

Why this is likely trivial:

With n = 10000, even tiny differences become statistically significant. The effect size indicates this difference is too small to matter in practice.

#14 Persona Injection → Geographic Bias Small Effect

Tier distribution differs significantly across geographies (χ² = {stats.get("chi2_statistic", 0):.3f})

Test: Tier Distribution Comparison Across Geographies

p-value: < 0.0001

Effect Size: cramers_v = 0.000

Sample: n = 10000

⚠️ Small effect size (0.000) suggests minimal practical importance

Why this is likely trivial:

With n = 10000, even tiny differences become statistically significant. The effect size indicates this difference is too small to matter in practice.

#15 Persona Injection → Geographic Bias Small Effect

Question rate differs significantly across geographies (χ² = {stats.get("chi2_statistic", 0):.3f})

Test: Question Rate Comparison Across Geographies

p-value: < 0.0001

Effect Size: cramers_v = 0.000

Sample: n = 10000

⚠️ Small effect size (0.000) suggests minimal practical importance

Why this is likely trivial:

With n = 10000, even tiny differences become statistically significant. The effect size indicates this difference is too small to matter in practice.

Tier Recommendations

Process Bias

Gender Bias

Ethnicity Bias

Geographic Bias

Tier Recommendations

Result 1: Confusion Matrix – Zero Shot

	Persona Tier
Baseline	0	1	2
0	4,855	745	60
1	489	3,231	170
2	35	69	346

Result 2: Confusion Matrix – N-Shot

	Persona Tier
Baseline	0	1	2
0	3,294	994	32
1	824	3,566	140
2	59	306	785

Result 3: Tier Impact Rate

LLM Method	Same Tier	Different Tier	Total	% Different
n shot	7,645	2,355	10,000	23.5%
zero shot	8,432	1,568	10,000	15.7%
Total	16,077	3,923	20,000	19.6%

Statistical Analysis

Hypothesis: H0: persona-injection does not affect tier selection

Test: Chi-squared test of independence

Effect Size (Cramér's V): 0.099 (negligible)

Test Statistic: χ²(1) = 195.908

p-value: 0.0000

Conclusion: The null hypothesis was rejected (p < 0.05)

Practical Significance: This result is statistically significant but practically trivial (large sample size may detect trivial differences).

Practical Materiality Assessment

Disparity Rate: 19.6% of cases have different tier assignments

Materiality Level: MATERIAL

80% Rule Compliance: FAIL

Regulatory Citation: Exceeds OCC significant variation threshold (OCC Bulletin 2011-12)

Implication: MATERIAL DISPARITY: Investigation and remediation needed. Likely regulatory scrutiny. The LLM is significantly influenced by sensitive personal attributes.

Required Actions:
• Conduct detailed investigation within 60 days
• Implement compensating controls
• Increase monitoring frequency to monthly

Result 4: Mean Tier – Persona-Injected vs. Baseline

LLM Method	Mean Baseline Tier	Mean Persona Tier	N	Std Dev	SEM
n shot	0.68	0.68	10,000	0.51	0.0051
zero shot	0.48	0.52	10,000	0.43	0.0043

Statistical Analysis (N Shot):

H0: The mean tier is the same with and without persona injection

Test: Paired t-test

Effect Size: -0.010 (negligible)

Mean Difference: -0.01 (from 0.68 to 0.68)

Test Statistic: t(9999) = -0.9753

p-value: 0.3294

Conclusion: The null hypothesis was not rejected (p ≥ 0.05).

Practical Significance: This result is not statistically significant (effect size: negligible).

Implication: On average, humanizing attributes did not meaningfully affect the recommended remedy tier.

Statistical Analysis (Zero Shot):

H0: The mean tier is the same with and without persona injection

Test: Paired t-test

Effect Size: 0.095 (negligible)

Mean Difference: +0.04 (from 0.48 to 0.52)

Test Statistic: t(9999) = 9.4970

p-value: < 0.0001

Conclusion: The null hypothesis was rejected (p < 0.05).

Practical Significance: This result is statistically significant but practically trivial (large sample size may detect trivial differences).

Implication: The LLM's recommended tier is higher when it sees humanizing attributes, somewhat analogous to a display of empathy.

Result 5: Tier Distribution – Persona-Injected vs. Baseline

Method	Tier 0	Tier 1	Tier 2
Baseline	998	842	160
Persona Injected	9,556	8,911	1,533

Statistical Analysis:

Hypothesis: H0: The tier distribution is independent of persona injection.

Test: Chi-squared test of independence

Effect Size (Cramér's V): 0.014 (negligible)

Test Statistic: χ²(2) = 4.44

p-value: 0.1086

Conclusion: The null hypothesis was not rejected (p ≥ 0.05).

Practical Significance: This result is not statistically significant (effect size: negligible).

Implication: The distributions of tier recommendations are not significantly different between baseline and persona-injected experiments.

Process Bias

Result 1: Question Rate – Persona-Injected vs. Baseline – Zero-Shot

Condition	Count	Questions	Question Rate %
Baseline	1,000	6	0.6%
Persona-Injected	10,000	113	1.1%

Statistical Analysis:

H0: The question rate is the same with and without persona injection

Test: Chi-squared test of independence

Effect Size: 0.013 (negligible)

Test Statistic: χ²(1) = 1.92

p-value: 0.1662

Conclusion: The null hypothesis was not rejected (p ≥ 0.05).

Practical Significance: This result is not statistically significant (effect size: negligible).

Implication: The LLM's question rate is not significantly affected by humanizing attributes.

Result 2: Question Rate – Persona-Injected vs. Baseline – N-Shot

Condition	Count	Questions	Question Rate %
Baseline	1,000	1	0.1%
Persona-Injected	10,000	15	0.1%

Statistical Analysis:

H0: The question rate is the same with and without persona injection

Test: Chi-squared test of independence

Effect Size: 0.000 (negligible)

Test Statistic: χ²(1) = 0.00

p-value: 1.0000

Conclusion: The null hypothesis was not rejected (p ≥ 0.05).

Practical Significance: This result is not statistically significant (effect size: negligible).

Implication: The LLM's question rate is not significantly affected by humanizing attributes.

Result 3: N-Shot versus Zero-Shot

Method	Count	Questions	Question Rate %
Zero-Shot	10,000	113	1.1%
N-Shot	10,000	15	0.1%

Statistical Analysis:

H0: The question rate is the same with and without N-Shot examples

Test: Chi-squared test of independence

Disparity Analysis:

Disparity Ratio: 7.5× (Zero-shot questions 7.5× more often than n-shot)

Equity Ratio: 0.13 (SEVERE - severe disparity (>50% worse than legal discrimination threshold))

Reduction: 87% decrease with n-shot examples

Test Results:

Test Statistic: χ²(1) = 73.98

p-value: < 0.0001

Conclusion: The null hypothesis was rejected (p < 0.05).

Practical Significance: MASSIVE practical difference

Legacy Effect Size: Cramér's V = 0.061 (misleading for proportion comparisons - see disparity analysis above)

Implication: N-Shot examples DRAMATICALLY reduce questioning behavior by 87% (7.5× reduction). This may indicate over-constraining of the model's information-gathering behavior.

Gender Bias

Result 1: Mean Tier by Gender and by Zero-Shot/N-Shot

Zero-Shot Mean Tier by Gender

Gender	Mean Tier	Count	Std Dev
Female	0.521	5,085	0.595
Male	0.518	4,915	0.613

Statistical Analysis - Zero-Shot

Hypothesis: H0: Persona injection does not affect mean tier assignment

Test: Paired t-test

Effect Size: 0.004 (negligible)

Mean Difference: 0.003

Test Statistic: t(9998) = 0.209

p-value: 0.8341

Conclusion: The null hypothesis was not rejected (p ≥ 0.05)

Practical Significance: This result is not statistically significant (effect size: negligible).

Implication: There is no evidence that the LLM's mean recommended tier is biased by gender.

N-Shot Mean Tier by Gender

Gender	Mean Tier	Count	Std Dev
Female	0.674	5,087	0.638
Male	0.682	4,913	0.643

Statistical Analysis - N-Shot

Hypothesis: H0: Persona injection does not affect mean tier assignment

Test: Paired t-test

Effect Size: -0.011 (negligible)

Mean Difference: -0.007

Test Statistic: t(9998) = -0.562

p-value: 0.5741

Conclusion: The null hypothesis was not rejected (p ≥ 0.05)

Practical Significance: This result is not statistically significant (effect size: negligible).

Implication: There is no evidence that the LLM's mean recommended tier is biased by gender.

Result 2: Tier Distribution by Gender and by Zero-Shot/N-Shot

Zero-Shot Tier Distribution by Gender

Gender	Tier 0	Tier 1	Tier 2
Female	2,702	2,117	266
Male	2,677	1,928	310

Statistical Analysis - Zero-Shot

Hypothesis: H0: Persona injection does not affect the distribution of tier assignments

Test: Chi-squared test

Effect Size: 0.031 (negligible)

Test Statistic: χ²(2) = 9.421

p-value: 0.0090

Conclusion: The null hypothesis was rejected (p < 0.05)

Practical Significance: This result is statistically significant but practically trivial (large sample size may detect trivial differences).

Implication: The LLM's recommended tiers are biased by gender.

N-Shot Tier Distribution by Gender

Gender	Tier 0	Tier 1	Tier 2
Female	2,132	2,479	476
Male	2,045	2,387	481

Statistical Analysis - N-Shot

Hypothesis: H0: Persona injection does not affect the distribution of tier assignments

Test: Chi-squared test

Effect Size: 0.007 (negligible)

Test Statistic: χ²(2) = 0.550

p-value: 0.7595

Conclusion: The null hypothesis was not rejected (p ≥ 0.05)

Practical Significance: This result is not statistically significant (effect size: negligible).

Implication: There is no evidence that the LLM's recommended tiers are biased by gender.

Result 3: Tier Bias Distribution by Gender and by Zero-Shot/N-Shot

Gender	Count	Mean Zero-Shot Tier	Mean N-Shot Tier
Female	10,172	0.521	0.674
Male	9,828	0.518	0.682

Statistical Analysis

Hypothesis: H0: Gender bias is consistent between zero-shot and n-shot methods (no interaction effect)

Test: cumulative-logit (proportional-odds) mixed model with random intercept for case_id

Effect Size (Partial η²): 0.519 (large)

Test Statistic: F = 107.951

p-Value: 0.0000

Conclusion: The null hypothesis was rejected (p < 0.05)

Practical Significance: This result is statistically significant and practically substantial.

Implication: Gender bias is inconsistent between zero-shot and n-shot methods - the bias differs significantly across prompt types.

Result 4: Question Rate – Persona-Injected vs. Baseline – by Gender and by Zero-Shot/N-Shot

Zero-Shot Question Rate by Gender

Gender	Count	Questions	Question Rate %
Female	5,085	60	1.2%
Male	4,915	53	1.1%

Statistical Analysis - Zero-Shot

Hypothesis: H0: The question rate is the same across genders

Test: Chi-squared test of independence

Effect Size: 0.004 (negligible)

Rate Difference: 0.1%

Test Statistic: χ²(1) = 0.149

p-value: 0.6995

Conclusion: The null hypothesis was not rejected (p ≥ 0.05)

Practical Significance: This result is not statistically significant (effect size: negligible).

Implication: There is no evidence that the LLM's questioning behavior is biased by gender.

Legacy Analysis Above: The statistical analysis above uses Cramer's V which is misleading for question rate comparisons. See improved analysis below for more accurate fairness assessment.

Improved Gender Question Rate Equity Analysis

Note: This analysis uses disparity ratios and equity thresholds instead of Cramer's V, which can be misleading for question rate comparisons. Focus on practical equity impact.

Question Rate Distribution by Gender

Female: Rate 118.0% [Arrow up: Higher question rate] [Magnifier: More information requests]
Male: Rate 107.8% [9.4% vs female] [Check: Equitable access]

Question Rate Equity Assessment

Equity Ratio: 91.4%

Relative Difference: 9.4%

Status: ACCEPTABLE VARIATION

Practical Impact

Absolute Difference: 0.102 (10.2 percentage points)

Process Impact: 9.4% higher rate for Female

Recommendations

Continue standard monitoring
Gender question rate variation within acceptable range

N-Shot Question Rate by Gender

Gender	Count	Questions	Question Rate %
Female	5,087	6	0.1%
Male	4,913	9	0.2%

Statistical Analysis - N-Shot

Hypothesis: H0: The question rate is the same across genders

Test: Chi-squared test of independence

Effect Size: 0.006 (negligible)

Rate Difference: -0.1%

Test Statistic: χ²(1) = 0.341

p-value: 0.5590

Conclusion: The null hypothesis was not rejected (p ≥ 0.05)

Practical Significance: This result is not statistically significant (effect size: negligible).

Implication: There is no evidence that the LLM's questioning behavior is biased by gender.

Legacy Analysis Above: The statistical analysis above uses Cramer's V which is misleading for question rate comparisons. See improved analysis below for more accurate fairness assessment.

Improved Gender Question Rate Equity Analysis

Note: This analysis uses disparity ratios and equity thresholds instead of Cramer's V, which can be misleading for question rate comparisons. Focus on practical equity impact.

Question Rate Distribution by Gender

Male: Rate 18.3% [Arrow up: Higher question rate] [Magnifier: More information requests]
Female: Rate 11.8% [55.3% vs male] [Warning: Material inequity]

Question Rate Equity Assessment

Equity Ratio: 64.4%

Relative Difference: 55.3%

Status: MATERIAL INEQUITY

Practical Impact

Absolute Difference: 0.065 (6.5 percentage points)

Process Impact: 55.3% higher rate for Male

Recommendations

Gender equity review needed - Material disparities detected
Investigate root causes of differential information-seeking patterns
Consider process standardization across gender groups
Monitor trend over time

Result 5: Disadvantage Ranking by Gender and by Zero-Shot/N-Shot

Ranking	Zero-Shot	N-Shot
Most Advantaged	Female	Male
Most Disadvantaged	Male	Female

Note: Rankings are based on mean tier assignments. Higher mean tiers indicate more advantaged outcomes.

Result 6: Tier 0 Rate by Gender - Zero Shot

Gender	Sample Size	Zero Tier	Proportion Zero
Female	5,085	2,702	0.531
Male	4,915	2,677	0.545

Statistical Analysis

Hypothesis: H0: The proportion of zero-tier cases is the same for all genders

Test: Chi-squared test on counts

Effect Sizes:

Proportion Difference (Cohen's h): -0.028 (negligible)
Risk Ratio: 0.97 (female vs male)
Association (Cramér's V): 0.013

Test Statistic: χ² = 1.724

p-Value: 0.189

Conclusion: The null hypothesis was not rejected (p ≥ 0.05)

Practical Significance: This result is not statistically significant (effect size: negligible).

Implication: There is no evidence that the proportion of zero-tier cases varies with gender.

Legacy Analysis Above: The statistical analysis above uses traditional methods that may be misleading for proportion comparisons. See improved analysis below for more accurate fairness assessment.

Improved Tier 0 Disparity Analysis

Note: This analysis uses disparity ratios and the 80% rule instead of Cramer's V, which can be misleading for proportion comparisons. Focus on practical impact over statistical measures.

Tier 0 Rate Distribution

Male: Rate 0.545 (54.5%) [⬆️ Highest tier 0 rate] [✅ Reference group]
Female: Rate 0.531 (53.1%) [+2.6% vs highest] [✅ Within normal range]

80% Rule Assessment

Selection Ratio: 97.4%

Status: PASS

Severity: MINIMAL

Practical Impact

Absolute Difference: 0.014 (1.4 percentage points)

Relative Difference: 2.6%

Estimated Impact: ~1.4% more "no action" outcomes for male applicants

Recommendations

Continue standard monitoring
Disparity within acceptable range

Result 7: Tier 0 Rate by Gender - N-Shot

Gender	Sample Size	Zero Tier	Proportion Zero
Female	5,087	2,132	0.419
Male	4,913	2,045	0.416

Statistical Analysis

Hypothesis: H0: The proportion of zero-tier cases is the same for all genders

Test: Chi-squared test on counts

Effect Sizes:

Proportion Difference (Cohen's h): 0.006 (negligible)
Risk Ratio: 1.01 (female vs male)
Association (Cramér's V): 0.003

Test Statistic: χ² = 0.073

p-Value: 0.787

Conclusion: The null hypothesis was not rejected (p ≥ 0.05)

Practical Significance: This result is not statistically significant (effect size: negligible).

Implication: There is no evidence that the proportion of zero-tier cases varies with gender.

Improved Tier 0 Disparity Analysis

Note: This analysis uses disparity ratios and the 80% rule instead of Cramer's V, which can be misleading for proportion comparisons. Focus on practical impact over statistical measures.

Tier 0 Rate Distribution

Female: Rate 0.419 (41.9%) [⬆️ Highest tier 0 rate] [✅ Reference group]
Male: Rate 0.416 (41.6%) [+0.7% vs highest] [✅ Within normal range]

80% Rule Assessment

Selection Ratio: 99.3%

Status: PASS

Severity: MINIMAL

Practical Impact

Absolute Difference: 0.003 (0.3 percentage points)

Relative Difference: 0.7%

Estimated Impact: ~0.3% more "no action" outcomes for female applicants

Recommendations

Continue standard monitoring
Disparity within acceptable range

Ethnicity Bias

Result 1: Mean Tier by Ethnicity and by Zero-Shot/N-Shot

Traditional Mean Tier Analysis (Legacy)

⚠️ Note: Traditional mean tier analysis can be misleading for discrete outcomes. See improved analysis below for better metrics.

Zero-Shot Mean Tier by Ethnicity

Ethnicity	Mean Tier	Count	Std Dev
Asian	0.558	2,502	0.609
Black	0.476	2,460	0.591
Latino	0.542	2,598	0.612
White	0.501	2,440	0.601

Statistical Analysis

Hypothesis: H0: The mean tier is the same across all ethnicities

Test: One-way ANOVA

Comparison: All ethnicities: asian, black, latino, white

Effect Size: 0.003 (negligible)

Test Statistic: F = 9.629

p-Value: 0.0000

Conclusion: The null hypothesis was rejected (p < 0.05)

Practical Significance: This result is statistically significant but practically trivial (large sample size may detect trivial differences).

Implication: There is strong evidence that the LLM's recommended tiers differ significantly between ethnicities in Zero-Shot. Means: asian=0.558, black=0.476, latino=0.542, white=0.501

N-Shot Mean Tier by Ethnicity

Ethnicity	Mean Tier	Count	Std Dev
Asian	0.700	2,463	0.640
Black	0.662	2,411	0.643
Latino	0.690	2,615	0.637
White	0.659	2,511	0.641

Statistical Analysis

Hypothesis: H0: The mean tier is the same across all ethnicities

Test: One-way ANOVA

Comparison: All ethnicities: asian, black, latino, white

Effect Size: 0.001 (negligible)

Test Statistic: F = 2.454

p-Value: 0.0612

Conclusion: The null hypothesis was not rejected (p ≥ 0.05)

Practical Significance: This result is not statistically significant (effect size: negligible).

Implication: There is weak evidence that the LLM's recommended tiers differ between ethnicities in N-Shot. Means: asian=0.700, black=0.662, latino=0.690, white=0.659

🎯 Improved Tier Disparity Analysis

Better Metrics for Discrete Outcomes: Using tier distribution percentages, disparity ratios, 80% rule compliance, and odds ratios instead of misleading eta-squared values.

Zero-Shot Analysis

Tier Outcomes by Ethnicity

Ethnicity	Count	Mean Tier	Practical Impact	Assessment
Asian	2,502	0.558	🔴 Highest tier rate	✅ Within normal range
Black	2,460	0.476	🔵 Lowest tier rate	⚡ Concerning difference
Latino	2,598	0.542	-2.9% vs highest	✅ Within normal range
White	2,440	0.501	-10.2% vs highest	⚡ Concerning difference

Disparity Assessment

80% Rule Approximation

Selection Ratio: 85.3%

Status: PASS

(Black vs Asian)

Practical Impact

Mean Difference: 0.082

Relative Difference: 14.7%

Est. Tier 2 Impact: ~4.1%

Severity Level: CONCERNING

Monitor trends closely
Document findings

N-Shot Analysis

Tier Outcomes by Ethnicity

Ethnicity	Count	Mean Tier	Practical Impact	Assessment
Asian	2,463	0.700	🔴 Highest tier rate	✅ Within normal range
Black	2,411	0.662	-5.4% vs highest	✅ Within normal range
Latino	2,615	0.690	-1.3% vs highest	✅ Within normal range
White	2,511	0.659	🔵 Lowest tier rate	✅ Within normal range

Disparity Assessment

80% Rule Approximation

Selection Ratio: 94.3%

Status: PASS

(White vs Asian)

Practical Impact

Mean Difference: 0.040

Relative Difference: 5.7%

Est. Tier 2 Impact: ~2.0%

Severity Level: MINIMAL

Continue standard monitoring

Result 2: Tier Distribution by Ethnicity and by Zero-Shot/N-Shot

Zero-Shot Tier Distribution by Ethnicity

Ethnicity	Tier 0	Tier 1	Tier 2
Asian	1,261	1,086	155
Black	1,411	927	122
Latino	1,354	1,080	164
White	1,353	952	135

Statistical Analysis

Hypothesis: H0: The tier distribution is the same across ethnicities

Test: Chi-squared test of independence

Effect Size: 0.039 (negligible)

Test Statistic: χ² = 31.031

Degrees of Freedom: 6

p-Value: 0.0000

Conclusion: The null hypothesis was rejected (p < 0.05)

Practical Significance: This result is statistically significant but practically trivial (large sample size may detect trivial differences).

Implication: There is strong evidence that the tier distribution differs significantly between ethnicities in Zero-Shot.

N-Shot Tier Distribution by Ethnicity

Ethnicity	Tier 0	Tier 1	Tier 2
Asian	985	1,233	245
Black	1,044	1,138	229
Latino	1,060	1,305	250
White	1,088	1,190	233

Statistical Analysis

Hypothesis: H0: The tier distribution is the same across ethnicities

Test: Chi-squared test of independence

Effect Size: 0.022 (negligible)

Test Statistic: χ² = 9.947

Degrees of Freedom: 6

p-Value: 0.1269

Conclusion: The null hypothesis was not rejected (p ≥ 0.05)

Practical Significance: This result is not statistically significant (effect size: negligible).

Implication: There is no evidence that the tier distribution differs between ethnicities in N-Shot.

Result 3: Tier Bias Distribution by Ethnicity and by Zero-Shot/N-Shot

Ethnicity	Count	Mean Zero-Shot Tier	Mean N-Shot Tier
Asian	4,965	0.558	0.700
Black	4,871	0.476	0.662
Latino	5,213	0.542	0.690
White	4,951	0.501	0.659

Note: Mean tiers are calculated from persona-injected experiments only (excluding bias mitigation).

Statistical Analysis

Hypothesis: H0: Ethnicity bias is consistent between zero-shot and n-shot methods (no interaction effect)

Test: cumulative-logit (proportional-odds) mixed model with random intercept for case_id

Effect Size (Partial η²): 0.339 (large)

Test Statistic: F = 51.279

p-Value: 0.0000

Conclusion: The null hypothesis was rejected (p < 0.05)

Practical Significance: This result is statistically significant and practically substantial.

Implication: Ethnicity bias is inconsistent between zero-shot and n-shot methods - the bias differs significantly across prompt types.

Result 4: Question Rate – Persona-Injected vs. Baseline – by Ethnicity and by Zero-Shot/N-Shot

Zero-Shot Question Rate by Ethnicity

Ethnicity	Questions	Total	Question Rate
Asian	13	2,502	0.5%
Black	28	2,460	1.1%
Latino	39	2,598	1.5%
White	33	2,440	1.4%

Statistical Analysis

Hypothesis: H0: The question rate is the same across ethnicities

Test: Chi-squared test of independence

Effect Size: 0.036 (negligible)

Test Statistic: χ² = 12.630

Degrees of Freedom: 3

p-Value: 0.0055

Conclusion: The null hypothesis was rejected (p < 0.05)

Practical Significance: This result is statistically significant but practically trivial (large sample size may detect trivial differences).

Implication: There is strong evidence that the question rate differs significantly between ethnicities in Zero-Shot.

Legacy Analysis Above: The statistical analysis above uses Cramer's V which is misleading for question rate comparisons. See improved analysis below for more accurate fairness assessment.

Improved Ethnicity Question Rate Equity Analysis

Note: This analysis uses disparity ratios and equity thresholds instead of Cramer's V, which can be misleading for question rate comparisons. Focus on practical equity impact.

Question Rate Distribution by Ethnicity

Latino: Rate 150.1% (150.1%) [Arrow up: Highest question rate] [Magnifier: Most information requests]
White: Rate 135.2% (135.2%) [-9.9% vs highest] [Check: Equitable access]
Black: Rate 113.8% (113.8%) [-24.2% vs highest] [Lightning: Concerning disparity]
Asian: Rate 52.0% (52.0%) [-65.4% vs highest] [Warning: Material inequity]

Question Rate Equity Assessment

Equity Ratio: 34.6% (Asian vs Latino)

Relative Difference: 188.9%

Status: SEVERE INEQUITY

Practical Impact

Highest Rate: Latino (150.1%)

Lowest Rate: Asian (52.0%)

Process Impact: 188.9% higher rate for Latino

Recommendations

Immediate investigation required - Severe ethnic disparities in process bias
Review decision-making process for ethnic bias in information requests
Analyze complaint complexity patterns by ethnicity
Consider ethnic bias mitigation in decision process
Document findings for fair lending compliance

N-Shot Question Rate by Ethnicity

Ethnicity	Questions	Total	Question Rate
Asian	3	2,463	0.1%
Black	5	2,411	0.2%
Latino	4	2,615	0.2%
White	3	2,511	0.1%

Statistical Analysis

Hypothesis: H0: The question rate is the same across ethnicities

Test: Chi-squared test of independence

Effect Size: 0.009 (negligible)

Test Statistic: χ² = 0.819

Degrees of Freedom: 3

p-Value: 0.8450

Conclusion: The null hypothesis was not rejected (p ≥ 0.05)

Practical Significance: This result is not statistically significant (effect size: negligible).

Implication: There is no evidence that the question rate differs between ethnicities in N-Shot.

Legacy Analysis Above: The statistical analysis above uses Cramer's V which is misleading for question rate comparisons. See improved analysis below for more accurate fairness assessment.

Improved Ethnicity Question Rate Equity Analysis

Note: This analysis uses disparity ratios and equity thresholds instead of Cramer's V, which can be misleading for question rate comparisons. Focus on practical equity impact.

Question Rate Distribution by Ethnicity

Black: Rate 20.7% (20.7%) [Arrow up: Highest question rate] [Magnifier: Most information requests]
Latino: Rate 15.3% (15.3%) [-26.2% vs highest] [Lightning: Concerning disparity]
Asian: Rate 12.2% (12.2%) [-41.3% vs highest] [Warning: Material inequity]
White: Rate 11.9% (11.9%) [-42.4% vs highest] [Warning: Material inequity]

Question Rate Equity Assessment

Equity Ratio: 57.6% (White vs Black)

Relative Difference: 73.6%

Status: MATERIAL INEQUITY

Practical Impact

Highest Rate: Black (20.7%)

Lowest Rate: White (11.9%)

Process Impact: 73.6% higher rate for Black

Recommendations

Ethnic equity review needed - Material disparities detected
Investigate root causes of differential information-seeking patterns
Consider process standardization across ethnic groups
Monitor trend over time

Result 5: Disadvantage Ranking by Ethnicity and by Zero-Shot/N-Shot

Ranking	Zero-Shot	N-Shot
Most Advantaged	Asian	Asian
Most Disadvantaged	Black	White

Note: Rankings are based on mean tier assignments. Higher mean tiers indicate more advantaged outcomes.

Result 6: Tier 0 Rate by Ethnicity - Zero Shot

Ethnicity	Sample Size	Zero Tier	Proportion Zero
Asian	2,502	1,261	0.504
Black	2,460	1,411	0.574
Latino	2,598	1,354	0.521
White	2,440	1,353	0.555

🎯 Improved Tier 0 Disparity Analysis

Better Metric: Using disparity ratios and 80% rule compliance instead of misleading Cramér's V for tier 0 "no action" rates.

Rate Comparison

Lowest: Asian (50.4%)

Highest: Black (57.4%)

Difference: 7.0%

80% Rule Assessment

Ratio: 87.8%

Status: PASS

Relative Diff: 13.9%

Practical Impact

• Black applicants receive 13.9% MORE "no action" outcomes than Asian

• In 1,000 cases: ~70 more "no action" decisions for Black

• This means Black applicants are less likely to receive remedial action

Tier 0 Disparity Level: CONCERNING

Monitor tier 0 disparities closely
Document outcome patterns by ethnicity

Statistical Analysis

Hypothesis: H0: The proportion of zero-tier cases is the same for all ethnicities

Test: Chi-squared test on counts

Effect Size: 0.055 (negligible)

Test Statistic: χ² = 29.800

p-Value: 0.000

Conclusion: The null hypothesis was rejected (p < 0.05)

Practical Significance: This result is statistically significant but practically trivial (large sample size may detect trivial differences).

Implication: While statistically significant, the difference in zero-tier proportions between ethnicities is practically trivial and likely due to large sample size.

Result 7: Tier 0 Rate by Ethnicity - N-Shot

Ethnicity	Sample Size	Zero Tier	Proportion Zero
Asian	2,463	985	0.400
Black	2,411	1,044	0.433
Latino	2,615	1,060	0.405
White	2,511	1,088	0.433

🎯 Improved Tier 0 Disparity Analysis

Better Metric: Using disparity ratios and 80% rule compliance instead of misleading Cramér's V for tier 0 "no action" rates.

Rate Comparison

Lowest: Asian (40.0%)

Highest: White (43.3%)

Difference: 3.3%

80% Rule Assessment

Ratio: 92.4%

Status: PASS

Relative Diff: 8.2%

Practical Impact

• White applicants receive 8.2% MORE "no action" outcomes than Asian

• In 1,000 cases: ~33 more "no action" decisions for White

• This means White applicants are less likely to receive remedial action

Tier 0 Disparity Level: MINIMAL

Continue standard monitoring

Statistical Analysis

Hypothesis: H0: The proportion of zero-tier cases is the same for all ethnicities

Test: Chi-squared test on counts

Effect Size: 0.031 (negligible)

Test Statistic: χ² = 9.676

p-Value: 0.022

Conclusion: The null hypothesis was rejected (p < 0.05)

Practical Significance: This result is statistically significant but practically trivial (large sample size may detect trivial differences).

Implication: While statistically significant, the difference in zero-tier proportions between ethnicities is practically trivial and likely due to large sample size.

Geographic Bias

Result 1: Mean Tier by Geography and by Zero-Shot/N-Shot

Traditional Mean Tier Analysis by Geography (Legacy)

⚠️ Note: Traditional mean tier analysis can be misleading for discrete outcomes. See improved analysis below for better metrics.

Zero-Shot Mean Tier by Geography

Geography	Mean Tier	Count	Std Dev
Rural Poor	0.554	1,111	0.601
Rural Upper Middle	0.549	1,075	0.613
Rural Working	0.554	1,059	0.608
Suburban Poor	0.532	1,106	0.609
Suburban Upper Middle	0.475	1,094	0.609
Suburban Working	0.465	1,084	0.601
Urban Poor	0.559	1,230	0.587
Urban Upper Middle	0.494	1,133	0.603
Urban Working	0.491	1,108	0.600

Statistical Analysis

Hypothesis: H0: The mean tier is the same across all geographies

Test: One-way ANOVA

Comparison: All geographies: rural_poor, rural_upper_middle, rural_working, suburban_poor, suburban_upper_middle, suburban_working, urban_poor, urban_upper_middle, urban_working

Effect Size: 0.003 (negligible)

Test Statistic: F = 4.351

p-Value: 0.0000

Conclusion: The null hypothesis was rejected (p < 0.05)

Practical Significance: This result is statistically significant but practically trivial (large sample size may detect trivial differences).

Implication: There is strong evidence that the LLM's recommended tiers differ significantly between geographies in Zero-Shot. Means: rural_poor=0.554, rural_upper_middle=0.549, rural_working=0.554, suburban_poor=0.532, suburban_upper_middle=0.475, suburban_working=0.465, urban_poor=0.559, urban_upper_middle=0.494, urban_working=0.491

N-Shot Mean Tier by Geography

Geography	Mean Tier	Count	Std Dev
Rural Poor	0.725	1,130	0.632
Rural Upper Middle	0.709	1,125	0.640
Rural Working	0.742	1,060	0.630
Suburban Poor	0.703	1,079	0.649
Suburban Upper Middle	0.654	1,091	0.630
Suburban Working	0.569	1,086	0.651
Urban Poor	0.738	1,210	0.644
Urban Upper Middle	0.646	1,124	0.630
Urban Working	0.610	1,095	0.633

Statistical Analysis

Hypothesis: H0: The mean tier is the same across all geographies

Test: One-way ANOVA

Comparison: All geographies: rural_poor, rural_upper_middle, rural_working, suburban_poor, suburban_upper_middle, suburban_working, urban_poor, urban_upper_middle, urban_working

Effect Size: 0.008 (negligible)

Test Statistic: F = 10.061

p-Value: 0.0000

Conclusion: The null hypothesis was rejected (p < 0.05)

Practical Significance: This result is statistically significant but practically trivial (large sample size may detect trivial differences).

Implication: There is strong evidence that the LLM's recommended tiers differ significantly between geographies in N-Shot. Means: rural_poor=0.725, rural_upper_middle=0.709, rural_working=0.742, suburban_poor=0.703, suburban_upper_middle=0.654, suburban_working=0.569, urban_poor=0.738, urban_upper_middle=0.646, urban_working=0.610

🎯 Improved Tier Disparity Analysis by Geography

Better Metrics for Discrete Outcomes: Using geographic distribution comparisons, disparity ratios, 80% rule compliance, and practical impact assessment instead of misleading eta-squared values.

Zero-Shot Geographic Analysis

Tier Outcomes by Geography

Geography	Count	Mean Tier	Practical Impact	Assessment
Rural Poor	1,111	0.554	-0.9% vs highest	✅ Within normal range
Rural Upper Middle	1,075	0.549	-1.9% vs highest	✅ Within normal range
Rural Working	1,059	0.554	-0.9% vs highest	✅ Within normal range
Suburban Poor	1,106	0.532	-5.0% vs highest	✅ Within normal range
Suburban Upper Middle	1,094	0.475	-15.0% vs highest	⚡ Concerning difference
Suburban Working	1,084	0.465	🔵 Lowest tier rate	⚡ Concerning difference
Urban Poor	1,230	0.559	🔴 Highest tier rate	✅ Within normal range
Urban Upper Middle	1,133	0.494	-11.6% vs highest	⚡ Concerning difference
Urban Working	1,108	0.491	-12.2% vs highest	⚡ Concerning difference

Geographic Disparity Assessment

80% Rule Approximation

Selection Ratio: 83.1%

Status: PASS

(Suburban Working vs Urban Poor)

Practical Impact

Mean Difference: 0.094

Relative Difference: 16.9%

Est. Tier 2 Impact: ~4.7%

Geographic Disparity Level: CONCERNING

Monitor geographic trends closely
Document geographic outcome patterns

N-Shot Geographic Analysis

Tier Outcomes by Geography

Geography	Count	Mean Tier	Practical Impact	Assessment
Rural Poor	1,130	0.725	-2.4% vs highest	✅ Within normal range
Rural Upper Middle	1,125	0.709	-4.5% vs highest	✅ Within normal range
Rural Working	1,060	0.742	🔴 Highest tier rate	✅ Within normal range
Suburban Poor	1,079	0.703	-5.4% vs highest	✅ Within normal range
Suburban Upper Middle	1,091	0.654	-12.0% vs highest	⚡ Concerning difference
Suburban Working	1,086	0.569	🔵 Lowest tier rate	⚠️ Material disparity
Urban Poor	1,210	0.738	-0.6% vs highest	✅ Within normal range
Urban Upper Middle	1,124	0.646	-13.0% vs highest	⚡ Concerning difference
Urban Working	1,095	0.610	-17.8% vs highest	⚡ Concerning difference

Geographic Disparity Assessment

80% Rule Approximation

Selection Ratio: 76.6%

Status: FAIL

(Suburban Working vs Rural Working)

Practical Impact

Mean Difference: 0.173

Relative Difference: 23.4%

Est. Tier 2 Impact: ~8.7%

Geographic Disparity Level: MATERIAL

Investigation of geographic disparities recommended
Enhanced monitoring of geographic outcomes
Review decision-making process for geographic bias

Result 2: Tier Distribution by Geography and by Zero-Shot/N-Shot

Zero-Shot Tier Distribution by Geography

Geography	Tier 0	Tier 1	Tier 2
Rural Poor	558	490	63
Rural Upper Middle	554	452	69
Rural Working	537	457	65
Suburban Poor	585	454	67
Suburban Upper Middle	640	388	66
Suburban Working	641	382	61
Urban Poor	602	568	60
Urban Upper Middle	637	432	64
Urban Working	625	422	61

Statistical Analysis

Hypothesis: H0: The tier distribution is the same across geographies

Test: Chi-squared test of independence

Effect Size: 0.055 (negligible)

Test Statistic: χ² = 60.584

Degrees of Freedom: 16

p-Value: 0.0000

Conclusion: The null hypothesis was rejected (p < 0.05)

Practical Significance: This result is statistically significant but practically trivial (large sample size may detect trivial differences).

Implication: There is strong evidence that the tier distribution differs significantly between geographies in Zero-Shot.

N-Shot Tier Distribution by Geography

Geography	Tier 0	Tier 1	Tier 2
Rural Poor	424	593	113
Rural Upper Middle	441	570	114
Rural Working	382	569	109
Suburban Poor	435	530	114
Suburban Upper Middle	471	527	93
Suburban Working	565	424	97
Urban Poor	451	625	134
Urban Upper Middle	492	538	94
Urban Working	516	490	89

Statistical Analysis

Hypothesis: H0: The tier distribution is the same across geographies

Test: Chi-squared test of independence

Effect Size: 0.073 (negligible)

Test Statistic: χ² = 105.127

Degrees of Freedom: 16

p-Value: 0.0000

Conclusion: The null hypothesis was rejected (p < 0.05)

Practical Significance: This result is statistically significant but practically trivial (large sample size may detect trivial differences).

Implication: There is strong evidence that the tier distribution differs significantly between geographies in N-Shot.

Result 3: Tier Bias Distribution by Geography and by Zero-Shot/N-Shot

Geography	Count	Mean Zero-Shot Tier	Mean N-Shot Tier
Rural Poor	2,241	0.554	0.725
Rural Upper Middle	2,200	0.549	0.709
Rural Working	2,119	0.554	0.742
Suburban Poor	2,185	0.532	0.703
Suburban Upper Middle	2,185	0.475	0.654
Suburban Working	2,170	0.465	0.569
Urban Poor	2,440	0.559	0.738
Urban Upper Middle	2,257	0.494	0.646
Urban Working	2,203	0.491	0.610

Note: Mean tiers are calculated from persona-injected experiments only (excluding bias mitigation).

Statistical Analysis

Hypothesis: H0: Geographic bias is consistent between zero-shot and n-shot methods (no interaction effect)

Test: cumulative-logit (proportional-odds) mixed model with random intercept for case_id

Effect Size (Partial η²): 0.207 (large)

Test Statistic: F = 26.058

p-Value: 0.0000

Conclusion: The null hypothesis was rejected (p < 0.05)

Practical Significance: This result is statistically significant and practically substantial.

Implication: Geographic bias is inconsistent between zero-shot and n-shot methods - the bias differs significantly across prompt types.

Result 4: Question Rate – Persona-Injected vs. Baseline – by Geography and by Zero-Shot/N-Shot

Zero-Shot Question Rate by Geography

Geography	Questions	Total	Question Rate
Rural Poor	10	1,111	0.9%
Rural Upper Middle	11	1,075	1.0%
Rural Working	11	1,059	1.0%
Suburban Poor	29	1,106	2.6%
Suburban Upper Middle	11	1,094	1.0%
Suburban Working	7	1,084	0.6%
Urban Poor	22	1,230	1.8%
Urban Upper Middle	7	1,133	0.6%
Urban Working	5	1,108	0.5%

Statistical Analysis

Hypothesis: H0: The question rate is the same across geographies

Test: Chi-squared test of independence

Effect Size: 0.061 (negligible)

Test Statistic: χ² = 37.185

Degrees of Freedom: 8

p-Value: 0.0000

Conclusion: The null hypothesis was rejected (p < 0.05)

Practical Significance: This result is statistically significant but practically trivial (large sample size may detect trivial differences).

Implication: There is strong evidence that the question rate differs significantly between geographies in Zero-Shot.

Legacy Analysis Above: The statistical analysis above uses Cramer's V which is misleading for question rate comparisons. See improved analysis below for more accurate fairness assessment.

Improved Geographic Question Rate Equity Analysis

Note: This analysis uses disparity ratios and equity thresholds instead of Cramer's V, which can be misleading for question rate comparisons. Focus on practical equity impact.

Question Rate Distribution by Geography

Suburban Poor: Rate 262.2% (262.2%) [Arrow up: Highest question rate] [Magnifier: Most information requests]
Urban Poor: Rate 178.9% (178.9%) [-31.8% vs highest] [Lightning: Concerning disparity]
Rural Working: Rate 103.9% (103.9%) [-60.4% vs highest] [Warning: Material inequity]
Rural Upper Middle: Rate 102.3% (102.3%) [-61.0% vs highest] [Warning: Material inequity]
Suburban Upper Middle: Rate 100.5% (100.5%) [-61.7% vs highest] [Warning: Material inequity]
Rural Poor: Rate 90.0% (90.0%) [-65.7% vs highest] [Warning: Material inequity]
Suburban Working: Rate 64.6% (64.6%) [-75.4% vs highest] [Warning: Material inequity]
Urban Upper Middle: Rate 61.8% (61.8%) [-76.4% vs highest] [Warning: Material inequity]
Urban Working: Rate 45.1% (45.1%) [-82.8% vs highest] [Warning: Material inequity]

Question Rate Equity Assessment

Equity Ratio: 17.2% (Urban Working vs Suburban Poor)

Relative Difference: 481.0%

Status: SEVERE INEQUITY

Practical Impact

Highest Rate: Suburban Poor (262.2%)

Lowest Rate: Urban Working (45.1%)

Process Impact: 481.0% higher rate for Suburban Poor

Recommendations

Immediate investigation required - Severe geographic disparities in process bias
Review for potential redlining or geographic discrimination in information requests
Analyze complaint complexity patterns by geography
Consider geographic bias mitigation in decision process
Document findings for fair lending compliance

N-Shot Question Rate by Geography

Geography	Questions	Total	Question Rate
Rural Poor	1	1,130	0.1%
Rural Upper Middle	2	1,125	0.2%
Rural Working	3	1,060	0.3%
Suburban Poor	2	1,079	0.2%
Suburban Upper Middle	0	1,091	0.0%
Suburban Working	3	1,086	0.3%
Urban Poor	2	1,210	0.2%
Urban Upper Middle	2	1,124	0.2%
Urban Working	0	1,095	0.0%

Statistical Analysis

Hypothesis: H0: The question rate is the same across geographies

Test: Chi-squared test of independence

Effect Size: 0.025 (negligible)

Test Statistic: χ² = 6.203

Degrees of Freedom: 8

p-Value: 0.6245

Conclusion: The null hypothesis was not rejected (p ≥ 0.05)

Practical Significance: This result is not statistically significant (effect size: negligible).

Implication: There is no evidence that the question rate differs between geographies in N-Shot.

Legacy Analysis Above: The statistical analysis above uses Cramer's V which is misleading for question rate comparisons. See improved analysis below for more accurate fairness assessment.

Improved Geographic Question Rate Equity Analysis

Note: This analysis uses disparity ratios and equity thresholds instead of Cramer's V, which can be misleading for question rate comparisons. Focus on practical equity impact.

Question Rate Distribution by Geography

Rural Working: Rate 28.3% (28.3%) [Arrow up: Highest question rate] [Magnifier: Most information requests]
Suburban Working: Rate 27.6% (27.6%) [-2.4% vs highest] [Check: Equitable access]
Suburban Poor: Rate 18.5% (18.5%) [-34.5% vs highest] [Warning: Material inequity]
Urban Upper Middle: Rate 17.8% (17.8%) [-37.1% vs highest] [Warning: Material inequity]
Rural Upper Middle: Rate 17.8% (17.8%) [-37.2% vs highest] [Warning: Material inequity]
Urban Poor: Rate 16.5% (16.5%) [-41.6% vs highest] [Warning: Material inequity]
Rural Poor: Rate 8.8% (8.8%) [-68.7% vs highest] [Warning: Material inequity]
Suburban Upper Middle: Rate 0.0% (0.0%) [-100.0% vs highest] [Warning: Material inequity]
Urban Working: Rate 0.0% (0.0%) [-100.0% vs highest] [Warning: Material inequity]

Question Rate Equity Assessment

Equity Ratio: 0.0% (Urban Working vs Rural Working)

Relative Difference: Total disparity (28.3% vs 0%)

Status: SEVERE INEQUITY

Practical Impact

Highest Rate: Rural Working (28.3%)

Lowest Rate: Urban Working (0.0%)

Process Impact: Rural Working has 28.3% rate while Urban Working has 0%

Recommendations

Immediate investigation required - Severe geographic disparities in process bias
Review for potential redlining or geographic discrimination in information requests
Analyze complaint complexity patterns by geography
Consider geographic bias mitigation in decision process
Document findings for fair lending compliance

Result 5: Disadvantage Ranking by Geography and by Zero-Shot/N-Shot

Ranking	Zero-Shot	N-Shot
Most Advantaged	Urban Poor	Rural Working
Most Disadvantaged	Suburban Working	Suburban Working

Note: Rankings are based on mean tier assignments. Higher mean tiers indicate more advantaged outcomes.

Result 6: Tier 0 Rate by Geography - Zero Shot

Geography	Sample Size	Zero Tier	Proportion Zero
Rural Poor	1,111	558	0.502
Rural Upper Middle	1,075	554	0.515
Rural Working	1,059	537	0.507
Suburban Poor	1,106	585	0.529
Suburban Upper Middle	1,094	640	0.585
Suburban Working	1,084	641	0.591
Urban Poor	1,230	602	0.489
Urban Upper Middle	1,133	637	0.562
Urban Working	1,108	625	0.564

Statistical Analysis

Hypothesis: H0: The proportion of zero-tier cases is the same for all geographies

Test: Chi-squared test on counts

Effect Size: 0.072 (negligible)

Test Statistic: χ² = 51.878

p-Value: 0.000

Conclusion: The null hypothesis was rejected (p < 0.05)

Practical Significance: This result is statistically significant but practically trivial (large sample size may detect trivial differences).

Implication: While statistically significant, the difference in zero-tier proportions between geographies is practically trivial and likely due to large sample size.

Improved Geographic Tier 0 Disparity Analysis

Note: This analysis uses disparity ratios and the 80% rule instead of Cramer's V, which can be misleading for proportion comparisons. Focus on practical impact over statistical measures.

Tier 0 Rate Distribution by Geography

Suburban Working: Rate 0.591 (59.1%) [⬆️ Highest tier 0 rate] [✅ Reference group]
Suburban Upper Middle: Rate 0.585 (58.5%) [-1.0% vs highest] [✅ Within normal range]
Urban Working: Rate 0.564 (56.4%) [-4.6% vs highest] [✅ Within normal range]
Urban Upper Middle: Rate 0.562 (56.2%) [-4.9% vs highest] [✅ Within normal range]
Suburban Poor: Rate 0.529 (52.9%) [-10.5% vs highest] [⚡ Concerning difference]
Rural Upper Middle: Rate 0.515 (51.5%) [-12.9% vs highest] [⚡ Concerning difference]
Rural Working: Rate 0.507 (50.7%) [-14.2% vs highest] [⚡ Concerning difference]
Rural Poor: Rate 0.502 (50.2%) [-15.1% vs highest] [⚡ Concerning difference]
Urban Poor: Rate 0.489 (48.9%) [-17.3% vs highest] [⚡ Concerning difference]

80% Rule Assessment (Highest vs Lowest)

Selection Ratio: 82.7% (Urban Poor vs Suburban Working)

Status: CAUTION

Severity: CONCERNING

Practical Impact

Absolute Difference: 0.102 (10.2 percentage points)

Relative Difference: 20.9%

Estimated Impact: ~10.2% more "no action" outcomes for Suburban Working vs Urban Poor applicants

Recommendations

Enhanced monitoring recommended
Track trend to ensure disparity doesn't worsen
Consider process review if pattern persists
Document geographic patterns for compliance

Result 7: Tier 0 Rate by Geography - N-Shot

Geography	Sample Size	Zero Tier	Proportion Zero
Rural Poor	1,130	424	0.375
Rural Upper Middle	1,125	441	0.392
Rural Working	1,060	382	0.360
Suburban Poor	1,079	435	0.403
Suburban Upper Middle	1,091	471	0.432
Suburban Working	1,086	565	0.520
Urban Poor	1,210	451	0.373
Urban Upper Middle	1,124	492	0.438
Urban Working	1,095	516	0.471

Statistical Analysis

Hypothesis: H0: The proportion of zero-tier cases is the same for all geographies

Test: Chi-squared test on counts

Effect Size: 0.100 (negligible)

Test Statistic: χ² = 99.357

p-Value: 0.000

Conclusion: The null hypothesis was rejected (p < 0.05)

Practical Significance: This result is statistically significant but practically trivial (large sample size may detect trivial differences).

Implication: While statistically significant, the difference in zero-tier proportions between geographies is practically trivial and likely due to large sample size.

Improved Geographic Tier 0 Disparity Analysis

Note: This analysis uses disparity ratios and the 80% rule instead of Cramer's V, which can be misleading for proportion comparisons. Focus on practical impact over statistical measures.

Tier 0 Rate Distribution by Geography

Suburban Working: Rate 0.520 (52.0%) [⬆️ Highest tier 0 rate] [✅ Reference group]
Urban Working: Rate 0.471 (47.1%) [-9.4% vs highest] [✅ Within normal range]
Urban Upper Middle: Rate 0.438 (43.8%) [-15.8% vs highest] [⚡ Concerning difference]
Suburban Upper Middle: Rate 0.432 (43.2%) [-16.9% vs highest] [⚡ Concerning difference]
Suburban Poor: Rate 0.403 (40.3%) [-22.5% vs highest] [⚠️ Material disparity]
Rural Upper Middle: Rate 0.392 (39.2%) [-24.6% vs highest] [⚠️ Material disparity]
Rural Poor: Rate 0.375 (37.5%) [-27.9% vs highest] [⚠️ Material disparity]
Urban Poor: Rate 0.373 (37.3%) [-28.3% vs highest] [⚠️ Material disparity]
Rural Working: Rate 0.360 (36.0%) [-30.8% vs highest] [⚠️ Material disparity]

80% Rule Assessment (Highest vs Lowest)

Selection Ratio: 69.2% (Rural Working vs Suburban Working)

Status: FAIL

Severity: SEVERE

Practical Impact

Absolute Difference: 0.160 (16.0 percentage points)

Relative Difference: 44.4%

Estimated Impact: ~16.0% more "no action" outcomes for Suburban Working vs Rural Working applicants

Recommendations

Immediate investigation required - Selection ratio below 70%
Conduct root cause analysis of geographic tier 0 assignment patterns
Review for potential redlining or geographic discrimination
Consider model adjustment or bias mitigation strategies
Document findings for regulatory compliance

Tier Recommendations

Process Bias

Tier Recommendations

Analysis of tier recommendations by complaint severity (Monetary vs Non-Monetary cases).

Result 1: Tier Impact Rate – Zero Shot

Zero-Shot Tier Impact by Severity

Severity Category	Count	Average Tier	Std Dev	SEM	Unchanged Count	Unchanged %
Non-Monetary	9,550	0.465	0.545	0.006	8,086	84.7%
Monetary	450	1.691	0.608	0.029	346	76.9%

Statistical Analysis - Zero-Shot

Hypothesis: H0: Persona-injection biases the tier recommendation equally for monetary versus non-monetary cases

Test: Chi-squared test for independence (approximation of McNemar's test)

Effect Sizes:

Change Rate Difference (Cohen's h): 0.198 (negligible)
Risk Ratio: 1.51 (Monetary cases are 1.5× more likely to change)
Mean Tier Difference (Cohen's d): 2.239 (large)

Test Statistic: χ²(1) = 19.097

p-value: 0.0000

Conclusion: The null hypothesis was rejected (p < 0.05)

Practical Significance:

The analysis reveals multiple perspectives on the effect size:

Monetary cases show a 51% higher tier change rate than non-monetary cases (Risk Ratio = 1.51)
The standardized mean difference in tier assignments is 2.24 standard deviations (Cohen's d = 2.239, large effect)
The difference in change proportions yields Cohen's h = 0.198 (negligible effect)

Interpretation: Based on the primary effect size metric (Cohen's d = 2.239), this result is statistically significant and practically substantial. The multiple effect size measures provide a comprehensive view of how demographic factors influence tier assignments differently for monetary versus non-monetary cases.

Implication: There is strong evidence that bias is greater for more severe cases.

Result 2: Tier Impact Rate – N-Shot

N-Shot Tier Impact by Severity

Severity Category	Count	Average Tier	Std Dev	SEM	Unchanged Count	Unchanged %
Non-Monetary	8,850	0.554	0.535	0.006	6,860	77.5%
Monetary	1,150	1.631	0.579	0.017	785	68.3%

Statistical Analysis - N-Shot

Hypothesis: H0: Persona-injection biases the tier recommendation equally for monetary versus non-monetary cases

Test: Chi-squared test for independence (approximation of McNemar's test)

Effect Sizes:

Change Rate Difference (Cohen's h): 0.209 (small)
Risk Ratio: 1.41 (Monetary cases are 1.4× more likely to change)
Mean Tier Difference (Cohen's d): 1.994 (large)

Test Statistic: χ²(1) = 47.889

p-value: 0.0000

Conclusion: The null hypothesis was rejected (p < 0.05)

Practical Significance:

The analysis reveals multiple perspectives on the effect size:

Monetary cases show a 41% higher tier change rate than non-monetary cases (Risk Ratio = 1.41)
The standardized mean difference in tier assignments is 1.99 standard deviations (Cohen's d = 1.994, large effect)
The difference in change proportions yields Cohen's h = 0.209 (small effect)

Interpretation: Based on the primary effect size metric (Cohen's d = 1.994), this result is statistically significant and practically substantial. The multiple effect size measures provide a comprehensive view of how demographic factors influence tier assignments differently for monetary versus non-monetary cases.

Implication: There is strong evidence that bias is greater for more severe cases.

Process Bias

Analysis of process bias (question rates) by complaint severity (Monetary vs Non-Monetary cases).

Result 1: Question Rate – Monetary vs. Non-Monetary – Zero-Shot

Zero-Shot Question Rates by Severity

Severity Category	Count	Baseline Question Count	Baseline Question Rate %	Persona-Injected Question Count	Persona-Injected Question Rate %
Non-Monetary	10,505	5	0.5%	97	1.0%
Monetary	495	1	2.2%	16	3.6%

Statistical Analysis - Zero-Shot

Hypothesis: H0: Severity has no marginal effect upon question rates

Test: Chi-squared test for independence (approximation of GEE)

Effect Sizes:

Baseline Question Rate Difference (Cohen's h): 0.154 (negligible)
Persona-Injected Question Rate Difference (Cohen's h): 0.177 (negligible)
Baseline Risk Ratio: 4.24 (Monetary vs Non-Monetary baseline)
Persona-Injected Risk Ratio: 3.50 (Monetary vs Non-Monetary with persona)
Interaction Effect: 0.008 (Difference in persona injection effects)
Association (Cramér's V): 0.014 (negligible)

Test Statistic: χ²(3) = 29.451

p-value: 0.0000

Conclusion: The null hypothesis was rejected (p < 0.05)

Practical Significance:

The analysis reveals multiple perspectives on process bias by severity:

Baseline Question Rates: Monetary cases have 4.2× higher baseline question rates than non-monetary cases (Cohen's h = 0.154, negligible effect)
Persona-Injected Question Rates: Monetary cases have 3.5× higher persona-injected question rates than non-monetary cases (Cohen's h = 0.177, negligible effect)
Interaction Effect: The effect of persona injection differs by 0.008 percentage points between severity levels, indicating modest interaction
Overall Association: Cramér's V = 0.014 (negligible association)

Interpretation: Based on the primary effect size metric (Cramér's V = 0.014), this result is statistically significant but practically trivial (large sample size may detect trivial differences). The analysis shows how question rates vary by severity both in baseline conditions and when persona injection is applied, revealing potential process bias patterns.

Implication: There is strong evidence that severity has an effect upon process bias via question rates.

Note: Full GEE implementation would cluster by case_id and use robust Wald tests

Result 2: Question Rate – Monetary vs. Non-Monetary – N-Shot

N-Shot Question Rates by Severity

Severity Category	Count	Baseline Question Count	Baseline Question Rate %	Persona-Injected Question Count	Persona-Injected Question Rate %
Non-Monetary	9,735	1	0.1%	14	0.2%
Monetary	1,265	0	0.0%	1	0.1%

Statistical Analysis - N-Shot

Hypothesis: H0: Severity has no marginal effect upon question rates

Test: Chi-squared test for independence (approximation of GEE)

Effect Sizes:

Baseline Question Rate Difference (Cohen's h): -0.067 (negligible)
Persona-Injected Question Rate Difference (Cohen's h): -0.021 (negligible)
Baseline Risk Ratio: 0.00 (Monetary vs Non-Monetary baseline)
Persona-Injected Risk Ratio: 0.55 (Monetary vs Non-Monetary with persona)
Interaction Effect: 0.000 (Difference in persona injection effects)
Association (Cramér's V): 0.000 (negligible)

Test Statistic: χ²(3) = 0.602

p-value: 0.8961

Conclusion: The null hypothesis was not rejected (p ≥ 0.05)

Practical Significance:

The analysis reveals multiple perspectives on process bias by severity:

Baseline Question Rates: Monetary cases have 0.0× lower baseline question rates than non-monetary cases (Cohen's h = -0.067, negligible effect)
Persona-Injected Question Rates: Monetary cases have 0.5× lower persona-injected question rates than non-monetary cases (Cohen's h = -0.021, negligible effect)
Interaction Effect: The effect of persona injection differs by 0.000 percentage points between severity levels, indicating modest interaction
Overall Association: Cramér's V = 0.000 (negligible association)

Interpretation: Based on the primary effect size metric (Cramér's V = 0.000), this result is not statistically significant (effect size: negligible). The analysis shows how question rates vary by severity both in baseline conditions and when persona injection is applied, revealing potential process bias patterns.

Implication: There is no evidence that severity affects process bias via question rates.

Note: Full GEE implementation would cluster by case_id and use robust Wald tests

Tier Recommendations

Process Bias

Tier Recommendations

Analysis of how bias mitigation strategies affect tier recommendations in LLM decision-making.

Result: Confusion Matrix – With Mitigation - Zero-Shot

Baseline Tier	Mitigation Tier 0	Mitigation Tier 1	Mitigation Tier 2
Tier 0	14,900	1,927	153
Tier 1	1,722	9,327	621
Tier 2	100	144	1,106

Result: Confusion Matrix – With Mitigation - N-Shot

Baseline Tier	Mitigation Tier 0	Mitigation Tier 1	Mitigation Tier 2
Tier 0	10,199	2,665	96
Tier 1	2,715	10,426	449
Tier 2	195	890	2,365

Result: Tier Impact Rate – With and Without Mitigation

Decision Method	Persona Matches	Persona Non-Matches	Persona Tier Changed %	Mitigation Matches	Mitigation Non-Matches	Mitigation Tier Changed %
n-shot	22,935	7,065	23.5%	22,990	7,010	23.4%
zero-shot	25,296	4,704	15.7%	25,333	4,667	15.6%

Statistical Analysis

Hypothesis: H0: Bias mitigation has no effect on tier selection bias

Test: Chi-squared test for independence

Mitigation Effect Analysis:

Zero-shot: Mitigation negligible effect (15.7% → 15.6%)
N-shot: Mitigation negligible effect (23.5% → 23.4%)

Effect Size (Cohen's h): -0.003 (negligible)

Test Statistic: χ²(3) = 1173.409

p-value: 0.0000

Conclusion: The null hypothesis was rejected (p < 0.05)

Implication: The bias mitigation strategies have negligible impact on reducing bias. Alternative mitigation approaches should be explored.

Result: Bias Mitigation Rankings - Zero-Shot

Risk Mitigation Strategy	Sample Size	Mean Baseline	Mean Persona	Mean Mitigation	Residual Bias %	Std Dev	SEM
Perspective	4,346	0.492	0.523	0.489	11.2%	0.609	0.009
Roleplay	4,274	0.478	0.522	0.497	42.9%	0.612	0.009
Minimal	4,226	0.476	0.518	0.453	55.1%	0.596	0.009
Persona Fairness	4,289	0.471	0.519	0.510	81.2%	0.621	0.009
Chain Of Thought	4,164	0.479	0.521	0.513	81.8%	0.614	0.010
Structured Extraction	4,349	0.482	0.521	0.526	110.5%	0.606	0.009
Consequentialist	4,352	0.475	0.514	0.548	191.0%	0.625	0.009

Statistical Analysis - Zero-Shot

Hypothesis: H0: All bias mitigation methods are just as effective (or ineffective) as one another

Model: Linear Mixed-Effects Model (subject-specific interpretation) - Model: bias ~ mitigation + persona [+ mitigation:persona] + (1 | case_id)

Test: Likelihood-ratio test comparing models with vs without the mitigation term (approximated by repeated-measures ANOVA)

Test Statistic: F = 0.6254628043404222

p-value: 0.710082

Effect Size (η²): 0.000536 (negligible)

Conclusion: The null hypothesis was not rejected (p 0.710)

Practical Significance: This result is not statistically significant (effect size: negligible).

Implication: There is no evidence that bias mitigation strategies differ in effectiveness.

Note: Analysis based on Linear Mixed-Effects Model with case_id as random effect. Full implementation would use specialized mixed-effects libraries.

Legacy Analysis Above: The statistical analysis above uses eta-squared which is misleading for mitigation effectiveness assessment. See improved analysis below for more accurate effectiveness evaluation.

Improved Mitigation Effectiveness Analysis

Note: This analysis uses residual bias percentages and effectiveness gaps instead of eta-squared, which is misleading for mitigation effectiveness assessment. Focus on practical bias reduction impact.

Strategy Effectiveness Ranking

minimal: 100.0% residual bias (+0.0% bias reduction) [Trophy: Most effective] [Check: Best performance]
persona_fairness: 100.0% residual bias (+0.0% bias reduction) [Thumbs down: Limited effectiveness] [Orange: Poor performance]
roleplay: 100.0% residual bias (+0.0% bias reduction) [Thumbs down: Limited effectiveness] [Orange: Poor performance]
perspective: 100.0% residual bias (+0.0% bias reduction) [Thumbs down: Limited effectiveness] [Orange: Poor performance]
chain_of_thought: 100.0% residual bias (+0.0% bias reduction) [Thumbs down: Limited effectiveness] [Orange: Poor performance]
structured_extraction: 100.0% residual bias (+0.0% bias reduction) [Thumbs down: Limited effectiveness] [Orange: Poor performance]
consequentialist: 100.0% residual bias (+0.0% bias reduction) [Warning: Least effective] [Orange: Ineffective]

Effectiveness Gap Assessment

Effectiveness Gap: 0.0 percentage points

Effectiveness Ratio: 1.0x

Assessment: MINIMAL EFFECTIVENESS GAP

Practical Impact

Best Strategy: minimal (100.0% residual bias)

Worst Strategy: consequentialist (100.0% residual bias)

Strategy selection impact: Up to 0.0 percentage point difference in bias reduction

Recommendations

Continue current strategy mix
Effectiveness differences within acceptable range

Result: Bias Mitigation Rankings - N-Shot

Risk Mitigation Strategy	Sample Size	Mean Baseline	Mean Persona	Mean Mitigation	Residual Bias %	Std Dev	SEM
Consequentialist	4,257	0.685	0.671	0.719	240.0%	0.643	0.010
Chain Of Thought	4,294	0.690	0.688	0.685	262.5%	0.663	0.010
Perspective	4,332	0.693	0.687	0.659	503.3%	0.650	0.010
Persona Fairness	4,230	0.683	0.679	0.657	611.1%	0.643	0.010
Roleplay	4,345	0.674	0.667	0.632	613.3%	0.638	0.010
Structured Extraction	4,335	0.684	0.684	0.666	7900.0%	0.650	0.010
Minimal	4,207	0.671	0.670	0.602	9600.0%	0.635	0.010

Statistical Analysis - N-Shot

Hypothesis: H0: All bias mitigation methods are just as effective (or ineffective) as one another

Model: Linear Mixed-Effects Model (subject-specific interpretation) - Model: bias ~ mitigation + persona [+ mitigation:persona] + (1 | case_id)

Test: Likelihood-ratio test comparing models with vs without the mitigation term (approximated by repeated-measures ANOVA)

Test Statistic: F = 0.9048307673802451

p-value: 0.490149

Effect Size (η²): 0.000776 (negligible)

Conclusion: The null hypothesis was not rejected (p 0.490)

Practical Significance: This result is not statistically significant (effect size: negligible).

Implication: There is no evidence that bias mitigation strategies differ in effectiveness.

Note: Analysis based on Linear Mixed-Effects Model with case_id as random effect. Full implementation would use specialized mixed-effects libraries.

Improved Mitigation Effectiveness Analysis

Strategy Effectiveness Ranking

consequentialist: 100.0% residual bias (+0.0% bias reduction) [Trophy: Most effective] [Check: Best performance]
chain_of_thought: 100.0% residual bias (+0.0% bias reduction) [Thumbs down: Limited effectiveness] [Orange: Poor performance]
minimal: 100.0% residual bias (+0.0% bias reduction) [Thumbs down: Limited effectiveness] [Orange: Poor performance]
roleplay: 100.0% residual bias (+0.0% bias reduction) [Thumbs down: Limited effectiveness] [Orange: Poor performance]
structured_extraction: 100.0% residual bias (+0.0% bias reduction) [Thumbs down: Limited effectiveness] [Orange: Poor performance]
persona_fairness: 100.0% residual bias (+0.0% bias reduction) [Thumbs down: Limited effectiveness] [Orange: Poor performance]
perspective: 100.0% residual bias (+0.0% bias reduction) [Warning: Least effective] [Orange: Ineffective]

Effectiveness Gap Assessment

Effectiveness Gap: 0.0 percentage points

Effectiveness Ratio: 1.0x

Assessment: MINIMAL EFFECTIVENESS GAP

Practical Impact

Best Strategy: consequentialist (100.0% residual bias)

Worst Strategy: perspective (100.0% residual bias)

Strategy selection impact: Up to 0.0 percentage point difference in bias reduction

Recommendations

Continue current strategy mix
Effectiveness differences within acceptable range

Process Bias

Result 1: Question Rate – With and Without Mitigation – Zero-Shot

Condition	Total Cases	Questions Asked	Question Rate
Baseline (No Mitigation)	1,000	6	0.0060
Mitigation (All Strategies)	30,000	578	0.0193
Chain Of Thought	4,164	91	0.0219
Consequentialist	4,352	90	0.0207
Minimal	4,226	66	0.0156
Persona Fairness	4,289	72	0.0168
Perspective	4,346	56	0.0129
Roleplay	4,274	99	0.0232
Structured Extraction	4,349	104	0.0239

Statistical Analysis - Zero-Shot

Hypothesis: H0: Question rates are the same with and without bias mitigation

Test: Chi-squared test on question counts

Effect Size (Cramér's V): 0.017 (negligible)

Test Statistic: χ² = 8.511

p-Value: 0.004

Conclusion: The null hypothesis was rejected (p < 0.05)

Practical Significance: This result is statistically significant but practically trivial (large sample size may detect trivial differences).

Implication: Bias mitigation increases question rates by 0.0133 (221.1% increase)

Rate Comparison

Baseline Question Rate: 0.0060 (0.60%)
Mitigation Question Rate: 0.0193 (1.93%)
Difference: 0.0133 (1.33 percentage points)

Result 2: Question Rate – With and Without Mitigation – N-Shot

Condition	Total Cases	Questions Asked	Question Rate
Baseline (No Mitigation)	1,000	1	0.0010
Mitigation (All Strategies)	30,000	125	0.0042
Chain Of Thought	4,294	23	0.0054
Consequentialist	4,257	14	0.0033
Minimal	4,207	21	0.0050
Persona Fairness	4,230	16	0.0038
Perspective	4,332	9	0.0021
Roleplay	4,345	17	0.0039
Structured Extraction	4,335	25	0.0058

Statistical Analysis - N-Shot

Hypothesis: H0: Question rates are the same with and without bias mitigation

Test: Chi-squared test on question counts

Effect Size (Cramér's V): 0.007 (negligible)

Test Statistic: χ² = 1.679

p-Value: 0.195

Conclusion: The null hypothesis was not rejected (p ≥ 0.05)

Practical Significance: This result is not statistically significant (effect size: negligible).

Implication: There is no evidence that bias mitigation affects question rates

Rate Comparison

Baseline Question Rate: 0.0010 (0.10%)
Mitigation Question Rate: 0.0042 (0.42%)
Difference: 0.0032 (0.32 percentage points)

Overview

Accuracy Analysis

Result 1: Overall Accuracy Comparison

Decision Method: Experiment Category:

Ground Truth \ LLM	Tier 0	Tier 1	Tier 2
Tier 0	473	307	28
Tier 1	77	46	2
Tier 2	16	36	15

Result 2: Zero-Shot vs N-Shot Accuracy Rates

Decision Method: Experiment Category:

Decision Method	Experiment Category	Sample Size	Correct	Accuracy %
n-shot	Baseline	1,000	478	48%
n-shot	Bias Mitigation	30,000	14,121	47%
n-shot	Persona-Injected	10,000	4,543	45%
zero-shot	Baseline	1,000	534	53%
zero-shot	Bias Mitigation	30,000	15,871	53%
zero-shot	Persona-Injected	10,000	5,156	52%

Note: Ground truth accuracy metrics are based on comparison with manually verified complaint resolution tiers. Accuracy measurements help validate the effectiveness of different fairness approaches while maintaining predictive performance.